From df0dd1d031d2ab66cef33abc021ca19af16a5d5a Mon Sep 17 00:00:00 2001 From: Colton Botta <39240537+cgbotta@users.noreply.github.com> Date: Tue, 18 Oct 2022 11:40:24 +0100 Subject: [PATCH 01/24] Created new pattern for documentation. Ported first using turing doc from md to jmd --- .gitignore | 1 + .../00-getting-started/00_getting-started.jmd | 75 +++++++++++++++++++ src/TuringTutorials.jl | 23 +++--- 3 files changed, 88 insertions(+), 11 deletions(-) create mode 100644 docs/00-getting-started/00_getting-started.jmd diff --git a/.gitignore b/.gitignore index da2f6e430..936c851f2 100644 --- a/.gitignore +++ b/.gitignore @@ -13,3 +13,4 @@ Testing/ /*/*/jl_*/ /Manifest.toml /test/Manifest.toml +.vscode \ No newline at end of file diff --git a/docs/00-getting-started/00_getting-started.jmd b/docs/00-getting-started/00_getting-started.jmd new file mode 100644 index 000000000..dc9dca9ea --- /dev/null +++ b/docs/00-getting-started/00_getting-started.jmd @@ -0,0 +1,75 @@ +--- +title: Getting Started +permalink: /:collection/:name/ +redirect_from: docs/0-gettingstarted/ +weave_options: + error : false +--- + +# Getting Started + + +## Installation + +To use Turing, you need to install Julia first and then install Turing. + + +### Install Julia + +You will need to install Julia 1.3 or greater, which you can get from [the official Julia website](http://julialang.org/downloads/). + + +### Install Turing.jl + +Turing is an officially registered Julia package, so you can install a stable version of Turing by running the following in the Julia REPL: + +```julia +using Pkg +Pkg.add("Turing") +``` + +You can check if all tests pass by running + +```julia +Pkg.test("Turing") +``` + +## Example +Here's a simple example showing Turing in action. + +First, we can load the Turing and StatsPlots modules + +```julia +using Turing +using StatsPlots +``` + +Then, we define a simple Normal model with unknown mean and variance +```julia +@model function gdemo(x, y) + s² ~ InverseGamma(2, 3) + m ~ Normal(0, sqrt(s²)) + x ~ Normal(m, sqrt(s²)) + y ~ Normal(m, sqrt(s²)) +end +``` + +Then we can run sampler a sampler to collect results. In this case it is a Hamiltonian Monte Carlo sampler +```julia +chn = sample(gdemo(1.5, 2), HMC(0.1, 5), 1000) +``` + +We can use `describe` to summarise results +```julia +describe(chn) +``` + +Finally, we can plot the results +```julia +plot(chn) +``` + +The plots can also be saved +```julia +savefig("gdemo-plot.png") +``` diff --git a/src/TuringTutorials.jl b/src/TuringTutorials.jl index 7d440f1e9..391ddb253 100644 --- a/src/TuringTutorials.jl +++ b/src/TuringTutorials.jl @@ -20,6 +20,7 @@ function __init__() end function weave( + source_folder::AbstractString, folder::AbstractString, file::AbstractString; out_path_root::AbstractString=pwd(), @@ -33,15 +34,15 @@ function weave( ) end - target = joinpath(REPO_DIR, "tutorials", folder, file) + target = joinpath(REPO_DIR, source_folder, folder, file) @info("Weaving $(target)") # Activate project # TODO: use separate Julia process? - if isfile(joinpath(REPO_DIR, "tutorials", folder, "Project.toml")) && + if isfile(joinpath(REPO_DIR, source_folder, folder, "Project.toml")) && (:github in build || :html in build || :pdf in build) @info("Instantiating", folder) - Pkg.activate(joinpath(REPO_DIR, "tutorials", folder)) + Pkg.activate(joinpath(REPO_DIR, source_folder, folder)) Pkg.instantiate() Pkg.build() @@ -91,23 +92,23 @@ function weave( end # Weave all tutorials -function weave(; kwargs...) - for folder in readdir(joinpath(REPO_DIR, "tutorials")) - weave(folder; kwargs...) +function weave(source_folder::AbstractString; kwargs...) + for folder in readdir(joinpath(REPO_DIR, source_folder)) + weave(source_folder, folder; kwargs...) end end # Weave a folder of tutorials -function weave(folder::AbstractString; kwargs...) - for file in readdir(joinpath(REPO_DIR, "tutorials", folder)) +function weave(source_folder::AbstractString, folder::AbstractString; kwargs...) + for file in readdir(joinpath(REPO_DIR, source_folder, folder)) # Skip non-`.jmd` files endswith(file, ".jmd") || continue - weave(folder, file; kwargs...) + weave(source_folder, folder, file; kwargs...) end end -function tutorial_footer(folder=nothing, file=nothing) +function doc_footer(folder=nothing, file=nothing) display( Markdown.md""" ## Appendix @@ -116,7 +117,7 @@ These tutorials are a part of the TuringTutorials repository, found at: Date: Tue, 18 Oct 2022 13:36:17 +0100 Subject: [PATCH 02/24] Updated style --- docs/00-getting-started/00_getting-started.jmd | 17 ++++++++++------- 1 file changed, 10 insertions(+), 7 deletions(-) diff --git a/docs/00-getting-started/00_getting-started.jmd b/docs/00-getting-started/00_getting-started.jmd index dc9dca9ea..140e0fe5e 100644 --- a/docs/00-getting-started/00_getting-started.jmd +++ b/docs/00-getting-started/00_getting-started.jmd @@ -8,17 +8,14 @@ weave_options: # Getting Started - ## Installation To use Turing, you need to install Julia first and then install Turing. - ### Install Julia You will need to install Julia 1.3 or greater, which you can get from [the official Julia website](http://julialang.org/downloads/). - ### Install Turing.jl Turing is an officially registered Julia package, so you can install a stable version of Turing by running the following in the Julia REPL: @@ -35,6 +32,7 @@ Pkg.test("Turing") ``` ## Example + Here's a simple example showing Turing in action. First, we can load the Turing and StatsPlots modules @@ -45,31 +43,36 @@ using StatsPlots ``` Then, we define a simple Normal model with unknown mean and variance + ```julia @model function gdemo(x, y) - s² ~ InverseGamma(2, 3) - m ~ Normal(0, sqrt(s²)) - x ~ Normal(m, sqrt(s²)) - y ~ Normal(m, sqrt(s²)) + s² ~ InverseGamma(2, 3) + m ~ Normal(0, sqrt(s²)) + x ~ Normal(m, sqrt(s²)) + return y ~ Normal(m, sqrt(s²)) end ``` Then we can run sampler a sampler to collect results. In this case it is a Hamiltonian Monte Carlo sampler + ```julia chn = sample(gdemo(1.5, 2), HMC(0.1, 5), 1000) ``` We can use `describe` to summarise results + ```julia describe(chn) ``` Finally, we can plot the results + ```julia plot(chn) ``` The plots can also be saved + ```julia savefig("gdemo-plot.png") ``` From fc6bf5b9c53f32f78f93eb110a9b152c8b41cadb Mon Sep 17 00:00:00 2001 From: Colton Botta <39240537+cgbotta@users.noreply.github.com> Date: Wed, 19 Oct 2022 14:10:18 +0100 Subject: [PATCH 03/24] Added default weave that handles both directories and addressed grammar in comments --- docs/00-getting-started/00_getting-started.jmd | 4 ++-- src/TuringTutorials.jl | 12 ++++++++++-- 2 files changed, 12 insertions(+), 4 deletions(-) diff --git a/docs/00-getting-started/00_getting-started.jmd b/docs/00-getting-started/00_getting-started.jmd index 140e0fe5e..4cf2e8b2f 100644 --- a/docs/00-getting-started/00_getting-started.jmd +++ b/docs/00-getting-started/00_getting-started.jmd @@ -25,7 +25,7 @@ using Pkg Pkg.add("Turing") ``` -You can check if all tests pass by running +You can check if all tests pass by running (it might take a long time) ```julia Pkg.test("Turing") @@ -53,7 +53,7 @@ Then, we define a simple Normal model with unknown mean and variance end ``` -Then we can run sampler a sampler to collect results. In this case it is a Hamiltonian Monte Carlo sampler +Then we can run a sampler to collect results. In this case, it is a Hamiltonian Monte Carlo sampler ```julia chn = sample(gdemo(1.5, 2), HMC(0.1, 5), 1000) diff --git a/src/TuringTutorials.jl b/src/TuringTutorials.jl index 391ddb253..f7ac1f3b7 100644 --- a/src/TuringTutorials.jl +++ b/src/TuringTutorials.jl @@ -10,6 +10,7 @@ using Pkg: Pkg const REPO_DIR = dirname(@__DIR__) const CSS_FILE = joinpath(REPO_DIR, "templates", "skeleton_css.css") const LATEX_FILE = joinpath(REPO_DIR, "templates", "julia_tex.tpl") +const WEAVE_DIRECTORIES = ["tutorials", "docs"] const DEFAULT_BUILD = (:script, :html, :github) @@ -91,14 +92,21 @@ function weave( end end -# Weave all tutorials +# Weave all tutorials and documentation +function weave(; kwargs...) + for source_folder in WEAVE_DIRECTORIES + weave(source_folder; kwargs...) + end +end + +# Weave all folders in specified directory function weave(source_folder::AbstractString; kwargs...) for folder in readdir(joinpath(REPO_DIR, source_folder)) weave(source_folder, folder; kwargs...) end end -# Weave a folder of tutorials +# Weave a specific tutorials or documentation function weave(source_folder::AbstractString, folder::AbstractString; kwargs...) for file in readdir(joinpath(REPO_DIR, source_folder, folder)) # Skip non-`.jmd` files From f760b32ecf46e0a2d0fa06f45df5d013680d9dc7 Mon Sep 17 00:00:00 2001 From: Colton Botta <39240537+cgbotta@users.noreply.github.com> Date: Thu, 20 Oct 2022 14:32:40 +0100 Subject: [PATCH 04/24] Renamed tutorials and added a doc example --- src/TuringTutorials.jl | 36 ++++++++----------- .../00_getting-started.jmd | 0 .../00_introduction.jmd | 0 .../Manifest.toml | 0 .../Project.toml | 0 .../01_gaussian-mixture-model.jmd | 0 .../Manifest.toml | 0 .../Project.toml | 0 .../02_logistic-regression.jmd | 0 .../Manifest.toml | 0 .../Project.toml | 0 .../03_bayesian-neural-network.jmd | 0 .../Manifest.toml | 0 .../Project.toml | 0 .../04_hidden-markov-model.jmd | 0 .../Manifest.toml | 0 .../Project.toml | 0 .../05_linear-regression.jmd | 0 .../Manifest.toml | 0 .../Project.toml | 0 .../06_infinite-mixture-model.jmd | 0 .../Manifest.toml | 0 .../Project.toml | 0 .../07_poisson-regression.jmd | 0 .../Manifest.toml | 0 .../Project.toml | 0 .../08_multinomial-logistic-regression.jmd | 0 .../Manifest.toml | 0 .../Project.toml | 0 .../09_variational-inference.jmd | 0 .../Manifest.toml | 0 .../Project.toml | 0 .../10_bayesian-differential-equations.jmd | 0 .../Manifest.toml | 0 .../Project.toml | 0 .../11_probabilistic-pca.jmd | 0 .../Manifest.toml | 0 .../Project.toml | 0 .../12_gaussian-process.jmd | 0 .../Manifest.toml | 0 .../Project.toml | 0 .../13_seasonal_time_series.jmd | 0 .../Manifest.toml | 0 .../Project.toml | 0 .../14_minituring.jmd | 0 .../Manifest.toml | 0 .../Project.toml | 0 47 files changed, 14 insertions(+), 22 deletions(-) rename {docs/00-getting-started => tutorials/docs-00-getting-started}/00_getting-started.jmd (100%) rename tutorials/{00-introduction => tutorial-00-introduction}/00_introduction.jmd (100%) rename tutorials/{00-introduction => tutorial-00-introduction}/Manifest.toml (100%) rename tutorials/{00-introduction => tutorial-00-introduction}/Project.toml (100%) rename tutorials/{01-gaussian-mixture-model => tutorial-01-gaussian-mixture-model}/01_gaussian-mixture-model.jmd (100%) rename tutorials/{01-gaussian-mixture-model => tutorial-01-gaussian-mixture-model}/Manifest.toml (100%) rename tutorials/{01-gaussian-mixture-model => tutorial-01-gaussian-mixture-model}/Project.toml (100%) rename tutorials/{02-logistic-regression => tutorial-02-logistic-regression}/02_logistic-regression.jmd (100%) rename tutorials/{02-logistic-regression => tutorial-02-logistic-regression}/Manifest.toml (100%) rename tutorials/{02-logistic-regression => tutorial-02-logistic-regression}/Project.toml (100%) rename tutorials/{03-bayesian-neural-network => tutorial-03-bayesian-neural-network}/03_bayesian-neural-network.jmd (100%) rename tutorials/{03-bayesian-neural-network => tutorial-03-bayesian-neural-network}/Manifest.toml (100%) rename tutorials/{03-bayesian-neural-network => tutorial-03-bayesian-neural-network}/Project.toml (100%) rename tutorials/{04-hidden-markov-model => tutorial-04-hidden-markov-model}/04_hidden-markov-model.jmd (100%) rename tutorials/{04-hidden-markov-model => tutorial-04-hidden-markov-model}/Manifest.toml (100%) rename tutorials/{04-hidden-markov-model => tutorial-04-hidden-markov-model}/Project.toml (100%) rename tutorials/{05-linear-regression => tutorial-05-linear-regression}/05_linear-regression.jmd (100%) rename tutorials/{05-linear-regression => tutorial-05-linear-regression}/Manifest.toml (100%) rename tutorials/{05-linear-regression => tutorial-05-linear-regression}/Project.toml (100%) rename tutorials/{06-infinite-mixture-model => tutorial-06-infinite-mixture-model}/06_infinite-mixture-model.jmd (100%) rename tutorials/{06-infinite-mixture-model => tutorial-06-infinite-mixture-model}/Manifest.toml (100%) rename tutorials/{06-infinite-mixture-model => tutorial-06-infinite-mixture-model}/Project.toml (100%) rename tutorials/{07-poisson-regression => tutorial-07-poisson-regression}/07_poisson-regression.jmd (100%) rename tutorials/{07-poisson-regression => tutorial-07-poisson-regression}/Manifest.toml (100%) rename tutorials/{07-poisson-regression => tutorial-07-poisson-regression}/Project.toml (100%) rename tutorials/{08-multinomial-logistic-regression => tutorial-08-multinomial-logistic-regression}/08_multinomial-logistic-regression.jmd (100%) rename tutorials/{08-multinomial-logistic-regression => tutorial-08-multinomial-logistic-regression}/Manifest.toml (100%) rename tutorials/{08-multinomial-logistic-regression => tutorial-08-multinomial-logistic-regression}/Project.toml (100%) rename tutorials/{09-variational-inference => tutorial-09-variational-inference}/09_variational-inference.jmd (100%) rename tutorials/{09-variational-inference => tutorial-09-variational-inference}/Manifest.toml (100%) rename tutorials/{09-variational-inference => tutorial-09-variational-inference}/Project.toml (100%) rename tutorials/{10-bayesian-differential-equations => tutorial-10-bayesian-differential-equations}/10_bayesian-differential-equations.jmd (100%) rename tutorials/{10-bayesian-differential-equations => tutorial-10-bayesian-differential-equations}/Manifest.toml (100%) rename tutorials/{10-bayesian-differential-equations => tutorial-10-bayesian-differential-equations}/Project.toml (100%) rename tutorials/{11-probabilistic-pca => tutorial-11-probabilistic-pca}/11_probabilistic-pca.jmd (100%) rename tutorials/{11-probabilistic-pca => tutorial-11-probabilistic-pca}/Manifest.toml (100%) rename tutorials/{11-probabilistic-pca => tutorial-11-probabilistic-pca}/Project.toml (100%) rename tutorials/{12-gaussian-process => tutorial-12-gaussian-process}/12_gaussian-process.jmd (100%) rename tutorials/{12-gaussian-process => tutorial-12-gaussian-process}/Manifest.toml (100%) rename tutorials/{12-gaussian-process => tutorial-12-gaussian-process}/Project.toml (100%) rename tutorials/{13-seasonal-time-series => tutorial-13-seasonal-time-series}/13_seasonal_time_series.jmd (100%) rename tutorials/{13-seasonal-time-series => tutorial-13-seasonal-time-series}/Manifest.toml (100%) rename tutorials/{13-seasonal-time-series => tutorial-13-seasonal-time-series}/Project.toml (100%) rename tutorials/{14-minituring => tutorial-14-minituring}/14_minituring.jmd (100%) rename tutorials/{14-minituring => tutorial-14-minituring}/Manifest.toml (100%) rename tutorials/{14-minituring => tutorial-14-minituring}/Project.toml (100%) diff --git a/src/TuringTutorials.jl b/src/TuringTutorials.jl index f7ac1f3b7..36c88fde6 100644 --- a/src/TuringTutorials.jl +++ b/src/TuringTutorials.jl @@ -10,7 +10,7 @@ using Pkg: Pkg const REPO_DIR = dirname(@__DIR__) const CSS_FILE = joinpath(REPO_DIR, "templates", "skeleton_css.css") const LATEX_FILE = joinpath(REPO_DIR, "templates", "julia_tex.tpl") -const WEAVE_DIRECTORIES = ["tutorials", "docs"] +const WEAVE_DIRECTORY = "tutorials" const DEFAULT_BUILD = (:script, :html, :github) @@ -21,7 +21,6 @@ function __init__() end function weave( - source_folder::AbstractString, folder::AbstractString, file::AbstractString; out_path_root::AbstractString=pwd(), @@ -35,15 +34,15 @@ function weave( ) end - target = joinpath(REPO_DIR, source_folder, folder, file) + target = joinpath(REPO_DIR, WEAVE_DIRECTORY, folder, file) @info("Weaving $(target)") # Activate project # TODO: use separate Julia process? - if isfile(joinpath(REPO_DIR, source_folder, folder, "Project.toml")) && + if isfile(joinpath(REPO_DIR, WEAVE_DIRECTORY, folder, "Project.toml")) && (:github in build || :html in build || :pdf in build) @info("Instantiating", folder) - Pkg.activate(joinpath(REPO_DIR, source_folder, folder)) + Pkg.activate(joinpath(REPO_DIR, WEAVE_DIRECTORY, folder)) Pkg.instantiate() Pkg.build() @@ -92,31 +91,24 @@ function weave( end end -# Weave all tutorials and documentation +# Weave all tutorials function weave(; kwargs...) - for source_folder in WEAVE_DIRECTORIES - weave(source_folder; kwargs...) + for folder in readdir(joinpath(REPO_DIR, WEAVE_DIRECTORY)) + weave(folder; kwargs...) end end -# Weave all folders in specified directory -function weave(source_folder::AbstractString; kwargs...) - for folder in readdir(joinpath(REPO_DIR, source_folder)) - weave(source_folder, folder; kwargs...) - end -end - -# Weave a specific tutorials or documentation -function weave(source_folder::AbstractString, folder::AbstractString; kwargs...) - for file in readdir(joinpath(REPO_DIR, source_folder, folder)) +# Weave a folder of tutorials +function weave(folder::AbstractString; kwargs...) + for file in readdir(joinpath(REPO_DIR, WEAVE_DIRECTORY, folder)) # Skip non-`.jmd` files endswith(file, ".jmd") || continue - weave(source_folder, folder, file; kwargs...) + weave(folder, file; kwargs...) end end -function doc_footer(folder=nothing, file=nothing) +function tutorial_footer(folder=nothing, file=nothing) display( Markdown.md""" ## Appendix @@ -125,7 +117,7 @@ These tutorials are a part of the TuringTutorials repository, found at: Date: Thu, 20 Oct 2022 14:43:22 +0100 Subject: [PATCH 05/24] fixed format --- src/TuringTutorials.jl | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/TuringTutorials.jl b/src/TuringTutorials.jl index 36c88fde6..6d31d4064 100644 --- a/src/TuringTutorials.jl +++ b/src/TuringTutorials.jl @@ -150,4 +150,4 @@ These tutorials are a part of the TuringTutorials repository, found at: Date: Thu, 20 Oct 2022 15:57:49 +0100 Subject: [PATCH 06/24] Reverted tutorial names back to originals --- .../00_introduction.jmd | 0 .../{tutorial-00-introduction => 00-introduction}/Manifest.toml | 0 .../{tutorial-00-introduction => 00-introduction}/Project.toml | 0 .../01_gaussian-mixture-model.jmd | 0 .../Manifest.toml | 0 .../Project.toml | 0 .../02_logistic-regression.jmd | 0 .../Manifest.toml | 0 .../Project.toml | 0 .../03_bayesian-neural-network.jmd | 0 .../Manifest.toml | 0 .../Project.toml | 0 .../04_hidden-markov-model.jmd | 0 .../Manifest.toml | 0 .../Project.toml | 0 .../05_linear-regression.jmd | 0 .../Manifest.toml | 0 .../Project.toml | 0 .../06_infinite-mixture-model.jmd | 0 .../Manifest.toml | 0 .../Project.toml | 0 .../07_poisson-regression.jmd | 0 .../Manifest.toml | 0 .../Project.toml | 0 .../08_multinomial-logistic-regression.jmd | 0 .../Manifest.toml | 0 .../Project.toml | 0 .../09_variational-inference.jmd | 0 .../Manifest.toml | 0 .../Project.toml | 0 .../10_bayesian-differential-equations.jmd | 0 .../Manifest.toml | 0 .../Project.toml | 0 .../11_probabilistic-pca.jmd | 0 .../Manifest.toml | 0 .../Project.toml | 0 .../12_gaussian-process.jmd | 0 .../Manifest.toml | 0 .../Project.toml | 0 .../13_seasonal_time_series.jmd | 0 .../Manifest.toml | 0 .../Project.toml | 0 .../{tutorial-14-minituring => 14-minituring}/14_minituring.jmd | 0 tutorials/{tutorial-14-minituring => 14-minituring}/Manifest.toml | 0 tutorials/{tutorial-14-minituring => 14-minituring}/Project.toml | 0 45 files changed, 0 insertions(+), 0 deletions(-) rename tutorials/{tutorial-00-introduction => 00-introduction}/00_introduction.jmd (100%) rename tutorials/{tutorial-00-introduction => 00-introduction}/Manifest.toml (100%) rename tutorials/{tutorial-00-introduction => 00-introduction}/Project.toml (100%) rename tutorials/{tutorial-01-gaussian-mixture-model => 01-gaussian-mixture-model}/01_gaussian-mixture-model.jmd (100%) rename tutorials/{tutorial-01-gaussian-mixture-model => 01-gaussian-mixture-model}/Manifest.toml (100%) rename tutorials/{tutorial-01-gaussian-mixture-model => 01-gaussian-mixture-model}/Project.toml (100%) rename tutorials/{tutorial-02-logistic-regression => 02-logistic-regression}/02_logistic-regression.jmd (100%) rename tutorials/{tutorial-02-logistic-regression => 02-logistic-regression}/Manifest.toml (100%) rename tutorials/{tutorial-02-logistic-regression => 02-logistic-regression}/Project.toml (100%) rename tutorials/{tutorial-03-bayesian-neural-network => 03-bayesian-neural-network}/03_bayesian-neural-network.jmd (100%) rename tutorials/{tutorial-03-bayesian-neural-network => 03-bayesian-neural-network}/Manifest.toml (100%) rename tutorials/{tutorial-03-bayesian-neural-network => 03-bayesian-neural-network}/Project.toml (100%) rename tutorials/{tutorial-04-hidden-markov-model => 04-hidden-markov-model}/04_hidden-markov-model.jmd (100%) rename tutorials/{tutorial-04-hidden-markov-model => 04-hidden-markov-model}/Manifest.toml (100%) rename tutorials/{tutorial-04-hidden-markov-model => 04-hidden-markov-model}/Project.toml (100%) rename tutorials/{tutorial-05-linear-regression => 05-linear-regression}/05_linear-regression.jmd (100%) rename tutorials/{tutorial-05-linear-regression => 05-linear-regression}/Manifest.toml (100%) rename tutorials/{tutorial-05-linear-regression => 05-linear-regression}/Project.toml (100%) rename tutorials/{tutorial-06-infinite-mixture-model => 06-infinite-mixture-model}/06_infinite-mixture-model.jmd (100%) rename tutorials/{tutorial-06-infinite-mixture-model => 06-infinite-mixture-model}/Manifest.toml (100%) rename tutorials/{tutorial-06-infinite-mixture-model => 06-infinite-mixture-model}/Project.toml (100%) rename tutorials/{tutorial-07-poisson-regression => 07-poisson-regression}/07_poisson-regression.jmd (100%) rename tutorials/{tutorial-07-poisson-regression => 07-poisson-regression}/Manifest.toml (100%) rename tutorials/{tutorial-07-poisson-regression => 07-poisson-regression}/Project.toml (100%) rename tutorials/{tutorial-08-multinomial-logistic-regression => 08-multinomial-logistic-regression}/08_multinomial-logistic-regression.jmd (100%) rename tutorials/{tutorial-08-multinomial-logistic-regression => 08-multinomial-logistic-regression}/Manifest.toml (100%) rename tutorials/{tutorial-08-multinomial-logistic-regression => 08-multinomial-logistic-regression}/Project.toml (100%) rename tutorials/{tutorial-09-variational-inference => 09-variational-inference}/09_variational-inference.jmd (100%) rename tutorials/{tutorial-09-variational-inference => 09-variational-inference}/Manifest.toml (100%) rename tutorials/{tutorial-09-variational-inference => 09-variational-inference}/Project.toml (100%) rename tutorials/{tutorial-10-bayesian-differential-equations => 10-bayesian-differential-equations}/10_bayesian-differential-equations.jmd (100%) rename tutorials/{tutorial-10-bayesian-differential-equations => 10-bayesian-differential-equations}/Manifest.toml (100%) rename tutorials/{tutorial-10-bayesian-differential-equations => 10-bayesian-differential-equations}/Project.toml (100%) rename tutorials/{tutorial-11-probabilistic-pca => 11-probabilistic-pca}/11_probabilistic-pca.jmd (100%) rename tutorials/{tutorial-11-probabilistic-pca => 11-probabilistic-pca}/Manifest.toml (100%) rename tutorials/{tutorial-11-probabilistic-pca => 11-probabilistic-pca}/Project.toml (100%) rename tutorials/{tutorial-12-gaussian-process => 12-gaussian-process}/12_gaussian-process.jmd (100%) rename tutorials/{tutorial-12-gaussian-process => 12-gaussian-process}/Manifest.toml (100%) rename tutorials/{tutorial-12-gaussian-process => 12-gaussian-process}/Project.toml (100%) rename tutorials/{tutorial-13-seasonal-time-series => 13-seasonal-time-series}/13_seasonal_time_series.jmd (100%) rename tutorials/{tutorial-13-seasonal-time-series => 13-seasonal-time-series}/Manifest.toml (100%) rename tutorials/{tutorial-13-seasonal-time-series => 13-seasonal-time-series}/Project.toml (100%) rename tutorials/{tutorial-14-minituring => 14-minituring}/14_minituring.jmd (100%) rename tutorials/{tutorial-14-minituring => 14-minituring}/Manifest.toml (100%) rename tutorials/{tutorial-14-minituring => 14-minituring}/Project.toml (100%) diff --git a/tutorials/tutorial-00-introduction/00_introduction.jmd 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rename from tutorials/tutorial-05-linear-regression/Project.toml rename to tutorials/05-linear-regression/Project.toml diff --git a/tutorials/tutorial-06-infinite-mixture-model/06_infinite-mixture-model.jmd b/tutorials/06-infinite-mixture-model/06_infinite-mixture-model.jmd similarity index 100% rename from tutorials/tutorial-06-infinite-mixture-model/06_infinite-mixture-model.jmd rename to tutorials/06-infinite-mixture-model/06_infinite-mixture-model.jmd diff --git a/tutorials/tutorial-06-infinite-mixture-model/Manifest.toml b/tutorials/06-infinite-mixture-model/Manifest.toml similarity index 100% rename from tutorials/tutorial-06-infinite-mixture-model/Manifest.toml rename to tutorials/06-infinite-mixture-model/Manifest.toml diff --git a/tutorials/tutorial-06-infinite-mixture-model/Project.toml b/tutorials/06-infinite-mixture-model/Project.toml similarity index 100% rename from tutorials/tutorial-06-infinite-mixture-model/Project.toml rename to tutorials/06-infinite-mixture-model/Project.toml diff --git a/tutorials/tutorial-07-poisson-regression/07_poisson-regression.jmd b/tutorials/07-poisson-regression/07_poisson-regression.jmd similarity index 100% rename from tutorials/tutorial-07-poisson-regression/07_poisson-regression.jmd rename to tutorials/07-poisson-regression/07_poisson-regression.jmd diff --git a/tutorials/tutorial-07-poisson-regression/Manifest.toml b/tutorials/07-poisson-regression/Manifest.toml similarity index 100% rename from tutorials/tutorial-07-poisson-regression/Manifest.toml rename to tutorials/07-poisson-regression/Manifest.toml diff --git a/tutorials/tutorial-07-poisson-regression/Project.toml b/tutorials/07-poisson-regression/Project.toml similarity index 100% rename from tutorials/tutorial-07-poisson-regression/Project.toml rename to tutorials/07-poisson-regression/Project.toml diff --git a/tutorials/tutorial-08-multinomial-logistic-regression/08_multinomial-logistic-regression.jmd 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a/tutorials/tutorial-09-variational-inference/09_variational-inference.jmd b/tutorials/09-variational-inference/09_variational-inference.jmd similarity index 100% rename from tutorials/tutorial-09-variational-inference/09_variational-inference.jmd rename to tutorials/09-variational-inference/09_variational-inference.jmd diff --git a/tutorials/tutorial-09-variational-inference/Manifest.toml b/tutorials/09-variational-inference/Manifest.toml similarity index 100% rename from tutorials/tutorial-09-variational-inference/Manifest.toml rename to tutorials/09-variational-inference/Manifest.toml diff --git a/tutorials/tutorial-09-variational-inference/Project.toml b/tutorials/09-variational-inference/Project.toml similarity index 100% rename from tutorials/tutorial-09-variational-inference/Project.toml rename to tutorials/09-variational-inference/Project.toml diff --git a/tutorials/tutorial-10-bayesian-differential-equations/10_bayesian-differential-equations.jmd b/tutorials/10-bayesian-differential-equations/10_bayesian-differential-equations.jmd similarity index 100% rename from tutorials/tutorial-10-bayesian-differential-equations/10_bayesian-differential-equations.jmd rename to tutorials/10-bayesian-differential-equations/10_bayesian-differential-equations.jmd diff --git a/tutorials/tutorial-10-bayesian-differential-equations/Manifest.toml b/tutorials/10-bayesian-differential-equations/Manifest.toml similarity index 100% rename from tutorials/tutorial-10-bayesian-differential-equations/Manifest.toml rename to tutorials/10-bayesian-differential-equations/Manifest.toml diff --git a/tutorials/tutorial-10-bayesian-differential-equations/Project.toml b/tutorials/10-bayesian-differential-equations/Project.toml similarity index 100% rename from tutorials/tutorial-10-bayesian-differential-equations/Project.toml rename to tutorials/10-bayesian-differential-equations/Project.toml diff --git a/tutorials/tutorial-11-probabilistic-pca/11_probabilistic-pca.jmd b/tutorials/11-probabilistic-pca/11_probabilistic-pca.jmd similarity index 100% rename from tutorials/tutorial-11-probabilistic-pca/11_probabilistic-pca.jmd rename to tutorials/11-probabilistic-pca/11_probabilistic-pca.jmd diff --git a/tutorials/tutorial-11-probabilistic-pca/Manifest.toml b/tutorials/11-probabilistic-pca/Manifest.toml similarity index 100% rename from tutorials/tutorial-11-probabilistic-pca/Manifest.toml rename to tutorials/11-probabilistic-pca/Manifest.toml diff --git a/tutorials/tutorial-11-probabilistic-pca/Project.toml b/tutorials/11-probabilistic-pca/Project.toml similarity index 100% rename from tutorials/tutorial-11-probabilistic-pca/Project.toml rename to tutorials/11-probabilistic-pca/Project.toml diff --git a/tutorials/tutorial-12-gaussian-process/12_gaussian-process.jmd b/tutorials/12-gaussian-process/12_gaussian-process.jmd similarity index 100% rename from tutorials/tutorial-12-gaussian-process/12_gaussian-process.jmd rename to tutorials/12-gaussian-process/12_gaussian-process.jmd diff --git a/tutorials/tutorial-12-gaussian-process/Manifest.toml b/tutorials/12-gaussian-process/Manifest.toml similarity index 100% rename from tutorials/tutorial-12-gaussian-process/Manifest.toml rename to tutorials/12-gaussian-process/Manifest.toml diff --git a/tutorials/tutorial-12-gaussian-process/Project.toml b/tutorials/12-gaussian-process/Project.toml similarity index 100% rename from tutorials/tutorial-12-gaussian-process/Project.toml rename to tutorials/12-gaussian-process/Project.toml diff --git a/tutorials/tutorial-13-seasonal-time-series/13_seasonal_time_series.jmd b/tutorials/13-seasonal-time-series/13_seasonal_time_series.jmd similarity index 100% rename from tutorials/tutorial-13-seasonal-time-series/13_seasonal_time_series.jmd rename to tutorials/13-seasonal-time-series/13_seasonal_time_series.jmd diff --git a/tutorials/tutorial-13-seasonal-time-series/Manifest.toml b/tutorials/13-seasonal-time-series/Manifest.toml similarity index 100% rename from tutorials/tutorial-13-seasonal-time-series/Manifest.toml rename to tutorials/13-seasonal-time-series/Manifest.toml diff --git a/tutorials/tutorial-13-seasonal-time-series/Project.toml b/tutorials/13-seasonal-time-series/Project.toml similarity index 100% rename from tutorials/tutorial-13-seasonal-time-series/Project.toml rename to tutorials/13-seasonal-time-series/Project.toml diff --git a/tutorials/tutorial-14-minituring/14_minituring.jmd b/tutorials/14-minituring/14_minituring.jmd similarity index 100% rename from tutorials/tutorial-14-minituring/14_minituring.jmd rename to tutorials/14-minituring/14_minituring.jmd diff --git a/tutorials/tutorial-14-minituring/Manifest.toml b/tutorials/14-minituring/Manifest.toml similarity index 100% rename from tutorials/tutorial-14-minituring/Manifest.toml rename to tutorials/14-minituring/Manifest.toml diff --git a/tutorials/tutorial-14-minituring/Project.toml b/tutorials/14-minituring/Project.toml similarity index 100% rename from tutorials/tutorial-14-minituring/Project.toml rename to tutorials/14-minituring/Project.toml From b6fe397e88826b63ecb3b2ffb8890c245fda05cc Mon Sep 17 00:00:00 2001 From: Colton Botta <39240537+cgbotta@users.noreply.github.com> Date: Thu, 20 Oct 2022 17:11:36 +0100 Subject: [PATCH 07/24] Generated manifest and project files, updated GH Action --- .github/workflows/CompatHelper.yml | 2 +- .../docs-00-getting-started/Manifest.toml | 1582 +++++++++++++++++ .../docs-00-getting-started/Project.toml | 4 + 3 files changed, 1587 insertions(+), 1 deletion(-) create mode 100644 tutorials/docs-00-getting-started/Manifest.toml create mode 100644 tutorials/docs-00-getting-started/Project.toml diff --git a/.github/workflows/CompatHelper.yml b/.github/workflows/CompatHelper.yml index 21c9836c8..53736aa72 100644 --- a/.github/workflows/CompatHelper.yml +++ b/.github/workflows/CompatHelper.yml @@ -30,7 +30,7 @@ jobs: # push!(subdirs, d) # end # end - subdirs = ["", "tutorials/00-introduction", "tutorials/01-gaussian-mixture-model", "tutorials/04-hidden-markov-model", "tutorials/08-multinomial-logistic-regression", "tutorials/10-bayesian-differential-equations", "tutorials/11-probabilistic-pca", "tutorials/12-gaussian-process", "tutorials/13-seasonal-time-series", "tutorials/14-minituring"] + subdirs = ["", "tutorials/00-introduction", "tutorials/01-gaussian-mixture-model", "tutorials/04-hidden-markov-model", "tutorials/08-multinomial-logistic-regression", "tutorials/10-bayesian-differential-equations", "tutorials/11-probabilistic-pca", "tutorials/12-gaussian-process", "tutorials/13-seasonal-time-series", "tutorials/14-minituring", "tutorials/docs-00-getting-started"] CompatHelper.main(; subdirs) shell: julia --color=yes {0} env: diff --git a/tutorials/docs-00-getting-started/Manifest.toml b/tutorials/docs-00-getting-started/Manifest.toml 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"JLLWrappers", "Libdl", "Pkg", "Zlib_jll"] +git-tree-sha1 = "94d180a6d2b5e55e447e2d27a29ed04fe79eb30c" +uuid = "b53b4c65-9356-5827-b1ea-8c7a1a84506f" +version = "1.6.38+0" + +[[deps.libvorbis_jll]] +deps = ["Artifacts", "JLLWrappers", "Libdl", "Ogg_jll", "Pkg"] +git-tree-sha1 = "b910cb81ef3fe6e78bf6acee440bda86fd6ae00c" +uuid = "f27f6e37-5d2b-51aa-960f-b287f2bc3b7a" +version = "1.3.7+1" + +[[deps.nghttp2_jll]] +deps = ["Artifacts", "Libdl"] +uuid = "8e850ede-7688-5339-a07c-302acd2aaf8d" + +[[deps.p7zip_jll]] +deps = ["Artifacts", "Libdl"] +uuid = "3f19e933-33d8-53b3-aaab-bd5110c3b7a0" + +[[deps.x264_jll]] +deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"] +git-tree-sha1 = "4fea590b89e6ec504593146bf8b988b2c00922b2" +uuid = "1270edf5-f2f9-52d2-97e9-ab00b5d0237a" +version = "2021.5.5+0" + +[[deps.x265_jll]] +deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"] +git-tree-sha1 = "ee567a171cce03570d77ad3a43e90218e38937a9" +uuid = "dfaa095f-4041-5dcd-9319-2fabd8486b76" +version = "3.5.0+0" + +[[deps.xkbcommon_jll]] +deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Wayland_jll", "Wayland_protocols_jll", "Xorg_libxcb_jll", "Xorg_xkeyboard_config_jll"] +git-tree-sha1 = "9ebfc140cc56e8c2156a15ceac2f0302e327ac0a" +uuid = "d8fb68d0-12a3-5cfd-a85a-d49703b185fd" +version = "1.4.1+0" diff --git a/tutorials/docs-00-getting-started/Project.toml b/tutorials/docs-00-getting-started/Project.toml new file mode 100644 index 000000000..9b9db6a1f --- /dev/null +++ b/tutorials/docs-00-getting-started/Project.toml @@ -0,0 +1,4 @@ +[deps] +Pkg = "44cfe95a-1eb2-52ea-b672-e2afdf69b78f" +StatsPlots = "f3b207a7-027a-5e70-b257-86293d7955fd" +Turing = "fce5fe82-541a-59a6-adf8-730c64b5f9a0" From 59313b623cc98f555fe85d1c7e95f196da336fb0 Mon Sep 17 00:00:00 2001 From: Colton Botta <39240537+cgbotta@users.noreply.github.com> Date: Thu, 20 Oct 2022 17:35:13 +0100 Subject: [PATCH 08/24] Generated manifest with proper Julia version --- .../docs-00-getting-started/Manifest.toml | 605 +++++++++--------- 1 file changed, 302 insertions(+), 303 deletions(-) diff --git a/tutorials/docs-00-getting-started/Manifest.toml b/tutorials/docs-00-getting-started/Manifest.toml index 93c52293c..7313ce87a 100644 --- a/tutorials/docs-00-getting-started/Manifest.toml +++ b/tutorials/docs-00-getting-started/Manifest.toml @@ -1,1581 +1,1580 @@ # This file is machine-generated - editing it directly is not advised -julia_version = "1.7.0" -manifest_format = "2.0" - -[[deps.AbstractFFTs]] +[[AbstractFFTs]] deps = ["ChainRulesCore", "LinearAlgebra"] git-tree-sha1 = "69f7020bd72f069c219b5e8c236c1fa90d2cb409" uuid = "621f4979-c628-5d54-868e-fcf4e3e8185c" version = "1.2.1" -[[deps.AbstractMCMC]] +[[AbstractMCMC]] deps = ["BangBang", "ConsoleProgressMonitor", "Distributed", "Logging", "LoggingExtras", "ProgressLogging", "Random", "StatsBase", "TerminalLoggers", "Transducers"] git-tree-sha1 = "5c26c7759412ffcaf0dd6e3172e55d783dd7610b" uuid = "80f14c24-f653-4e6a-9b94-39d6b0f70001" version = "4.1.3" -[[deps.AbstractPPL]] +[[AbstractPPL]] deps = ["AbstractMCMC", "DensityInterface", "Setfield", "SparseArrays"] git-tree-sha1 = "6320752437e9fbf49639a410017d862ad64415a5" uuid = "7a57a42e-76ec-4ea3-a279-07e840d6d9cf" version = "0.5.2" -[[deps.AbstractTrees]] +[[AbstractTrees]] git-tree-sha1 = "52b3b436f8f73133d7bc3a6c71ee7ed6ab2ab754" uuid = "1520ce14-60c1-5f80-bbc7-55ef81b5835c" version = "0.4.3" -[[deps.Adapt]] +[[Adapt]] deps = ["LinearAlgebra"] git-tree-sha1 = "195c5505521008abea5aee4f96930717958eac6f" uuid = "79e6a3ab-5dfb-504d-930d-738a2a938a0e" version = "3.4.0" -[[deps.AdvancedHMC]] +[[AdvancedHMC]] deps = ["AbstractMCMC", "ArgCheck", "DocStringExtensions", "InplaceOps", "LinearAlgebra", "ProgressMeter", "Random", "Requires", "Setfield", "Statistics", "StatsBase", "StatsFuns", "UnPack"] git-tree-sha1 = "0091e2e4d0a7125da0e3ad8c7dbff9171a921461" uuid = "0bf59076-c3b1-5ca4-86bd-e02cd72cde3d" version = "0.3.6" -[[deps.AdvancedMH]] +[[AdvancedMH]] deps = ["AbstractMCMC", "Distributions", "Random", "Requires"] git-tree-sha1 = "d7a7dabeaef34e5106cdf6c2ac956e9e3f97f666" uuid = "5b7e9947-ddc0-4b3f-9b55-0d8042f74170" version = "0.6.8" -[[deps.AdvancedPS]] +[[AdvancedPS]] deps = ["AbstractMCMC", "Distributions", "Libtask", "Random", "StatsFuns"] git-tree-sha1 = "9ff1247be1e2aa2e740e84e8c18652bd9d55df22" uuid = "576499cb-2369-40b2-a588-c64705576edc" version = "0.3.8" -[[deps.AdvancedVI]] +[[AdvancedVI]] deps = ["Bijectors", "Distributions", "DistributionsAD", "DocStringExtensions", "ForwardDiff", "LinearAlgebra", "ProgressMeter", "Random", "Requires", "StatsBase", "StatsFuns", "Tracker"] git-tree-sha1 = "67fcc7d46c26250e89fc62798fbe07b5ee264c6f" uuid = "b5ca4192-6429-45e5-a2d9-87aec30a685c" version = "0.1.6" -[[deps.ArgCheck]] +[[ArgCheck]] git-tree-sha1 = "a3a402a35a2f7e0b87828ccabbd5ebfbebe356b4" uuid = "dce04be8-c92d-5529-be00-80e4d2c0e197" version = "2.3.0" -[[deps.ArgTools]] +[[ArgTools]] uuid = "0dad84c5-d112-42e6-8d28-ef12dabb789f" -[[deps.Arpack]] +[[Arpack]] deps = ["Arpack_jll", "Libdl", "LinearAlgebra", "Logging"] git-tree-sha1 = "91ca22c4b8437da89b030f08d71db55a379ce958" uuid = "7d9fca2a-8960-54d3-9f78-7d1dccf2cb97" version = "0.5.3" -[[deps.Arpack_jll]] -deps = ["Artifacts", "CompilerSupportLibraries_jll", "JLLWrappers", "Libdl", "OpenBLAS_jll", "Pkg"] -git-tree-sha1 = "5ba6c757e8feccf03a1554dfaf3e26b3cfc7fd5e" +[[Arpack_jll]] +deps = ["Libdl", "OpenBLAS_jll", "Pkg"] +git-tree-sha1 = "e214a9b9bd1b4e1b4f15b22c0994862b66af7ff7" uuid = "68821587-b530-5797-8361-c406ea357684" -version = "3.5.1+1" +version = "3.5.0+3" -[[deps.ArrayInterfaceCore]] +[[ArrayInterfaceCore]] deps = ["LinearAlgebra", "SparseArrays", "SuiteSparse"] git-tree-sha1 = "e9f7992287edfc27b3cbe0046c544bace004ca5b" uuid = "30b0a656-2188-435a-8636-2ec0e6a096e2" version = "0.1.22" -[[deps.ArrayInterfaceStaticArraysCore]] +[[ArrayInterfaceStaticArraysCore]] deps = ["Adapt", "ArrayInterfaceCore", "LinearAlgebra", "StaticArraysCore"] git-tree-sha1 = 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git-tree-sha1 = "e7fd7b2881fa2eaa72717420894d3938177862d1" uuid = "2def613f-5ad1-5310-b15b-b15d46f528f5" version = "0.4.0+1" -[[deps.Xorg_xcb_util_keysyms_jll]] +[[Xorg_xcb_util_keysyms_jll]] deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Xorg_xcb_util_jll"] git-tree-sha1 = "d1151e2c45a544f32441a567d1690e701ec89b00" uuid = "975044d2-76e6-5fbe-bf08-97ce7c6574c7" version = "0.4.0+1" -[[deps.Xorg_xcb_util_renderutil_jll]] +[[Xorg_xcb_util_renderutil_jll]] deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Xorg_xcb_util_jll"] git-tree-sha1 = "dfd7a8f38d4613b6a575253b3174dd991ca6183e" uuid = "0d47668e-0667-5a69-a72c-f761630bfb7e" version = "0.3.9+1" -[[deps.Xorg_xcb_util_wm_jll]] +[[Xorg_xcb_util_wm_jll]] deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Xorg_xcb_util_jll"] git-tree-sha1 = "e78d10aab01a4a154142c5006ed44fd9e8e31b67" uuid = "c22f9ab0-d5fe-5066-847c-f4bb1cd4e361" version = "0.4.1+1" -[[deps.Xorg_xkbcomp_jll]] +[[Xorg_xkbcomp_jll]] deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Xorg_libxkbfile_jll"] git-tree-sha1 = "4bcbf660f6c2e714f87e960a171b119d06ee163b" uuid = "35661453-b289-5fab-8a00-3d9160c6a3a4" version = "1.4.2+4" -[[deps.Xorg_xkeyboard_config_jll]] +[[Xorg_xkeyboard_config_jll]] deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Xorg_xkbcomp_jll"] git-tree-sha1 = "5c8424f8a67c3f2209646d4425f3d415fee5931d" uuid = "33bec58e-1273-512f-9401-5d533626f822" version = "2.27.0+4" -[[deps.Xorg_xtrans_jll]] +[[Xorg_xtrans_jll]] deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"] git-tree-sha1 = "79c31e7844f6ecf779705fbc12146eb190b7d845" uuid = "c5fb5394-a638-5e4d-96e5-b29de1b5cf10" version = "1.4.0+3" -[[deps.Zlib_jll]] +[[Zlib_jll]] deps = ["Libdl"] uuid = "83775a58-1f1d-513f-b197-d71354ab007a" -[[deps.Zstd_jll]] +[[Zstd_jll]] deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"] git-tree-sha1 = "e45044cd873ded54b6a5bac0eb5c971392cf1927" uuid = "3161d3a3-bdf6-5164-811a-617609db77b4" version = "1.5.2+0" -[[deps.ZygoteRules]] +[[ZygoteRules]] deps = ["MacroTools"] git-tree-sha1 = "8c1a8e4dfacb1fd631745552c8db35d0deb09ea0" uuid = "700de1a5-db45-46bc-99cf-38207098b444" version = "0.2.2" -[[deps.fzf_jll]] +[[fzf_jll]] deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"] git-tree-sha1 = "868e669ccb12ba16eaf50cb2957ee2ff61261c56" uuid = "214eeab7-80f7-51ab-84ad-2988db7cef09" version = "0.29.0+0" -[[deps.libaom_jll]] +[[libaom_jll]] deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"] git-tree-sha1 = "3a2ea60308f0996d26f1e5354e10c24e9ef905d4" uuid = "a4ae2306-e953-59d6-aa16-d00cac43593b" version = "3.4.0+0" -[[deps.libass_jll]] +[[libass_jll]] deps = ["Artifacts", "Bzip2_jll", "FreeType2_jll", "FriBidi_jll", "HarfBuzz_jll", "JLLWrappers", "Libdl", "Pkg", "Zlib_jll"] git-tree-sha1 = "5982a94fcba20f02f42ace44b9894ee2b140fe47" uuid = "0ac62f75-1d6f-5e53-bd7c-93b484bb37c0" version = "0.15.1+0" -[[deps.libblastrampoline_jll]] -deps = ["Artifacts", "Libdl", "OpenBLAS_jll"] -uuid = "8e850b90-86db-534c-a0d3-1478176c7d93" - -[[deps.libfdk_aac_jll]] +[[libfdk_aac_jll]] deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"] git-tree-sha1 = "daacc84a041563f965be61859a36e17c4e4fcd55" uuid = "f638f0a6-7fb0-5443-88ba-1cc74229b280" version = "2.0.2+0" -[[deps.libpng_jll]] +[[libpng_jll]] deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Zlib_jll"] git-tree-sha1 = "94d180a6d2b5e55e447e2d27a29ed04fe79eb30c" uuid = "b53b4c65-9356-5827-b1ea-8c7a1a84506f" version = "1.6.38+0" -[[deps.libvorbis_jll]] +[[libvorbis_jll]] deps = ["Artifacts", "JLLWrappers", "Libdl", "Ogg_jll", "Pkg"] git-tree-sha1 = "b910cb81ef3fe6e78bf6acee440bda86fd6ae00c" uuid = "f27f6e37-5d2b-51aa-960f-b287f2bc3b7a" version = "1.3.7+1" -[[deps.nghttp2_jll]] +[[nghttp2_jll]] deps = ["Artifacts", "Libdl"] uuid = "8e850ede-7688-5339-a07c-302acd2aaf8d" -[[deps.p7zip_jll]] +[[p7zip_jll]] deps = ["Artifacts", "Libdl"] uuid = "3f19e933-33d8-53b3-aaab-bd5110c3b7a0" -[[deps.x264_jll]] +[[x264_jll]] deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"] git-tree-sha1 = "4fea590b89e6ec504593146bf8b988b2c00922b2" uuid = "1270edf5-f2f9-52d2-97e9-ab00b5d0237a" version = "2021.5.5+0" -[[deps.x265_jll]] +[[x265_jll]] deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"] git-tree-sha1 = "ee567a171cce03570d77ad3a43e90218e38937a9" uuid = "dfaa095f-4041-5dcd-9319-2fabd8486b76" version = "3.5.0+0" -[[deps.xkbcommon_jll]] +[[xkbcommon_jll]] deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Wayland_jll", "Wayland_protocols_jll", "Xorg_libxcb_jll", "Xorg_xkeyboard_config_jll"] git-tree-sha1 = "9ebfc140cc56e8c2156a15ceac2f0302e327ac0a" uuid = "d8fb68d0-12a3-5cfd-a85a-d49703b185fd" From 1b1c241aa692da4321e8c68209f9d2058dc12746 Mon Sep 17 00:00:00 2001 From: Colton Botta <39240537+cgbotta@users.noreply.github.com> Date: Thu, 20 Oct 2022 20:16:45 +0100 Subject: [PATCH 09/24] Fixed project files --- tutorials/docs-00-getting-started/00_getting-started.jmd | 2 +- tutorials/docs-00-getting-started/Project.toml | 1 - 2 files changed, 1 insertion(+), 2 deletions(-) diff --git a/tutorials/docs-00-getting-started/00_getting-started.jmd b/tutorials/docs-00-getting-started/00_getting-started.jmd index 4cf2e8b2f..e46d99759 100644 --- a/tutorials/docs-00-getting-started/00_getting-started.jmd +++ b/tutorials/docs-00-getting-started/00_getting-started.jmd @@ -27,7 +27,7 @@ Pkg.add("Turing") You can check if all tests pass by running (it might take a long time) -```julia +```{julia; eval=false} Pkg.test("Turing") ``` diff --git a/tutorials/docs-00-getting-started/Project.toml b/tutorials/docs-00-getting-started/Project.toml index 9b9db6a1f..d9c1da059 100644 --- a/tutorials/docs-00-getting-started/Project.toml +++ b/tutorials/docs-00-getting-started/Project.toml @@ -1,4 +1,3 @@ [deps] -Pkg = "44cfe95a-1eb2-52ea-b672-e2afdf69b78f" StatsPlots = "f3b207a7-027a-5e70-b257-86293d7955fd" Turing = "fce5fe82-541a-59a6-adf8-730c64b5f9a0" From 2590aa4132ce8f4cc8acaae890f2fef2463f6753 Mon Sep 17 00:00:00 2001 From: Colton Botta <39240537+cgbotta@users.noreply.github.com> Date: Fri, 21 Oct 2022 17:08:39 +0100 Subject: [PATCH 10/24] Updated Tutorial --- .../00_getting-started.jmd | 45 +++++++++++-------- 1 file changed, 27 insertions(+), 18 deletions(-) diff --git a/tutorials/docs-00-getting-started/00_getting-started.jmd b/tutorials/docs-00-getting-started/00_getting-started.jmd index e46d99759..61cfc686c 100644 --- a/tutorials/docs-00-getting-started/00_getting-started.jmd +++ b/tutorials/docs-00-getting-started/00_getting-started.jmd @@ -25,11 +25,7 @@ using Pkg Pkg.add("Turing") ``` -You can check if all tests pass by running (it might take a long time) - -```{julia; eval=false} -Pkg.test("Turing") -``` +You can check if all tests pass by running `Pkg.test("Turing")` (it might take a long time) ## Example @@ -40,29 +36,44 @@ First, we can load the Turing and StatsPlots modules ```julia using Turing using StatsPlots +using Random ``` -Then, we define a simple Normal model with unknown mean and variance +Then, we define a simple Normal model with unknown mean and variance. +We assume that the variance follows an Inverse Guassian distribution, +while the mean follows a Normal distribution. The model has an +`obs` parameter that represents the observations (the data) that the +model is shown. ```julia -@model function gdemo(x, y) +@model function gdemo(obs) + # Our prior belief about the variance s² ~ InverseGamma(2, 3) + # Our prior belief about the mean m ~ Normal(0, sqrt(s²)) - x ~ Normal(m, sqrt(s²)) - return y ~ Normal(m, sqrt(s²)) + + # Iterate over each observation shown to the model + for n in 1:length(obs) + # Observation means are drawn from a Normal distribution. + obs[n] ~ Normal(m,s²) + end end ``` -Then we can run a sampler to collect results. In this case, it is a Hamiltonian Monte Carlo sampler +Now, let's see if our model can accurately estimate the mean and variance +of a normal distribution with mean 5 and variance 1. We can generate 100 +observations from such a distribution using the `rand` function. ```julia -chn = sample(gdemo(1.5, 2), HMC(0.1, 5), 1000) +Random.seed!(1) # Setting the seed +data = rand(Normal(5,1), 100) ``` -We can use `describe` to summarise results +Then we can use a Hamiltonian Monte Carlo (HMC) sampler to run inference and +collect results. We pass the data to our model and run 1000 iterations ```julia -describe(chn) +chn = sample(gdemo(data), HMC(0.1, 5), 1000) ``` Finally, we can plot the results @@ -71,8 +82,6 @@ Finally, we can plot the results plot(chn) ``` -The plots can also be saved - -```julia -savefig("gdemo-plot.png") -``` +Looking at the summary statistics and plots generated above, mean and variance should look +close to 5 and 1, respectively. This implies that we have successfully learned a model on the +data - but it is important to verify the results! Subsequent tutorials will explore this in more detail. From 7116f2915a50f81dd0459a23ba6b38b98247bf29 Mon Sep 17 00:00:00 2001 From: Colton Botta <39240537+cgbotta@users.noreply.github.com> Date: Fri, 21 Oct 2022 17:13:30 +0100 Subject: [PATCH 11/24] style --- tutorials/docs-00-getting-started/00_getting-started.jmd | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/tutorials/docs-00-getting-started/00_getting-started.jmd b/tutorials/docs-00-getting-started/00_getting-started.jmd index 61cfc686c..cf644266f 100644 --- a/tutorials/docs-00-getting-started/00_getting-started.jmd +++ b/tutorials/docs-00-getting-started/00_getting-started.jmd @@ -55,7 +55,7 @@ model is shown. # Iterate over each observation shown to the model for n in 1:length(obs) # Observation means are drawn from a Normal distribution. - obs[n] ~ Normal(m,s²) + obs[n] ~ Normal(m, s²) end end ``` @@ -66,7 +66,7 @@ observations from such a distribution using the `rand` function. ```julia Random.seed!(1) # Setting the seed -data = rand(Normal(5,1), 100) +data = rand(Normal(5, 1), 100) ``` Then we can use a Hamiltonian Monte Carlo (HMC) sampler to run inference and From eb5476e97c2e93e21a58a3ff0cb26e6036424b9d Mon Sep 17 00:00:00 2001 From: Colton Botta <39240537+cgbotta@users.noreply.github.com> Date: Fri, 21 Oct 2022 20:12:41 +0100 Subject: [PATCH 12/24] fix style --- tutorials/docs-00-getting-started/00_getting-started.jmd | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/tutorials/docs-00-getting-started/00_getting-started.jmd b/tutorials/docs-00-getting-started/00_getting-started.jmd index cf644266f..838e461c5 100644 --- a/tutorials/docs-00-getting-started/00_getting-started.jmd +++ b/tutorials/docs-00-getting-started/00_getting-started.jmd @@ -1,6 +1,6 @@ --- title: Getting Started -permalink: /:collection/:name/ +permalink: /docs/using-turing/get-started redirect_from: docs/0-gettingstarted/ weave_options: error : false @@ -51,7 +51,6 @@ model is shown. s² ~ InverseGamma(2, 3) # Our prior belief about the mean m ~ Normal(0, sqrt(s²)) - # Iterate over each observation shown to the model for n in 1:length(obs) # Observation means are drawn from a Normal distribution. From d4b5ca99702afc721d0886f415c128f7f0e42332 Mon Sep 17 00:00:00 2001 From: Colton Botta <39240537+cgbotta@users.noreply.github.com> Date: Fri, 21 Oct 2022 20:21:04 +0100 Subject: [PATCH 13/24] style --- tutorials/docs-00-getting-started/00_getting-started.jmd | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tutorials/docs-00-getting-started/00_getting-started.jmd b/tutorials/docs-00-getting-started/00_getting-started.jmd index 838e461c5..c76fd2e94 100644 --- a/tutorials/docs-00-getting-started/00_getting-started.jmd +++ b/tutorials/docs-00-getting-started/00_getting-started.jmd @@ -83,4 +83,4 @@ plot(chn) Looking at the summary statistics and plots generated above, mean and variance should look close to 5 and 1, respectively. This implies that we have successfully learned a model on the -data - but it is important to verify the results! Subsequent tutorials will explore this in more detail. +data - but it is important to verify the results! Subsequent tutorials will explore this in more detail. From 9bf0a0878c4eaccd71402bbde22fee3eced2e2cf Mon Sep 17 00:00:00 2001 From: Colton Botta <39240537+cgbotta@users.noreply.github.com> Date: Wed, 26 Oct 2022 16:09:57 +0100 Subject: [PATCH 14/24] Added calculation of true posterior mean --- .../00_getting-started.jmd | 54 +++++++++---------- 1 file changed, 26 insertions(+), 28 deletions(-) diff --git a/tutorials/docs-00-getting-started/00_getting-started.jmd b/tutorials/docs-00-getting-started/00_getting-started.jmd index c76fd2e94..cc7516c9e 100644 --- a/tutorials/docs-00-getting-started/00_getting-started.jmd +++ b/tutorials/docs-00-getting-started/00_getting-started.jmd @@ -1,6 +1,6 @@ --- title: Getting Started -permalink: /docs/using-turing/get-started +permalink: /:collection/:name/ redirect_from: docs/0-gettingstarted/ weave_options: error : false @@ -36,51 +36,49 @@ First, we can load the Turing and StatsPlots modules ```julia using Turing using StatsPlots -using Random ``` -Then, we define a simple Normal model with unknown mean and variance. -We assume that the variance follows an Inverse Guassian distribution, -while the mean follows a Normal distribution. The model has an -`obs` parameter that represents the observations (the data) that the -model is shown. +Then, we define a simple Normal model with unknown mean and variance ```julia -@model function gdemo(obs) - # Our prior belief about the variance +@model function gdemo(x, y) s² ~ InverseGamma(2, 3) - # Our prior belief about the mean m ~ Normal(0, sqrt(s²)) - # Iterate over each observation shown to the model - for n in 1:length(obs) - # Observation means are drawn from a Normal distribution. - obs[n] ~ Normal(m, s²) - end + x ~ Normal(m, sqrt(s²)) + return y ~ Normal(m, sqrt(s²)) end ``` -Now, let's see if our model can accurately estimate the mean and variance -of a normal distribution with mean 5 and variance 1. We can generate 100 -observations from such a distribution using the `rand` function. +Then we can run a sampler to collect results. In this case, it is a Hamiltonian Monte Carlo sampler ```julia -Random.seed!(1) # Setting the seed -data = rand(Normal(5, 1), 100) +chn = sample(gdemo(1.5, 2), HMC(0.1, 5), 1000) ``` -Then we can use a Hamiltonian Monte Carlo (HMC) sampler to run inference and -collect results. We pass the data to our model and run 1000 iterations +We can plot the results ```julia -chn = sample(gdemo(data), HMC(0.1, 5), 1000) +plot(chn) ``` -Finally, we can plot the results +In this case, because we use the [normal-inverse gamma distribution](https://en.wikipedia.org/wiki/Normal-inverse-gamma_distribution) +as a [conjugate prior](https://en.wikipedia.org/wiki/Conjugate_prior), we can compute +its updated mean as follows: ```julia -plot(chn) +s² = InverseGamma(2, 3) +m = Normal(0, 1) +data = [1.5,2] + +true_posterior_mean = (m.σ * m.μ + length(data) * mean(data)) / (m.σ + length(data)) ``` -Looking at the summary statistics and plots generated above, mean and variance should look -close to 5 and 1, respectively. This implies that we have successfully learned a model on the -data - but it is important to verify the results! Subsequent tutorials will explore this in more detail. +Finally, we can check if the true posterior mean aligns with our approximation + +```julia +# Visualize a blue density plot of the approximate posterior distribution +density(group(chn, :m);legend=:best, label="Estimated Mean", w=2, c=:blue) + +# Visualize the true posterior mean in red +vline!([true_posterior_mean]; label="True Mean", c=:red) +``` From f39de6cc3b7bcf475d6b433a535dcd5b2ddc8a7f Mon Sep 17 00:00:00 2001 From: Colton Botta <39240537+cgbotta@users.noreply.github.com> Date: Wed, 26 Oct 2022 16:13:58 +0100 Subject: [PATCH 15/24] format --- tutorials/docs-00-getting-started/00_getting-started.jmd | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/tutorials/docs-00-getting-started/00_getting-started.jmd b/tutorials/docs-00-getting-started/00_getting-started.jmd index cc7516c9e..33c2bf2e0 100644 --- a/tutorials/docs-00-getting-started/00_getting-started.jmd +++ b/tutorials/docs-00-getting-started/00_getting-started.jmd @@ -68,7 +68,7 @@ its updated mean as follows: ```julia s² = InverseGamma(2, 3) m = Normal(0, 1) -data = [1.5,2] +data = [1.5, 2] true_posterior_mean = (m.σ * m.μ + length(data) * mean(data)) / (m.σ + length(data)) ``` @@ -77,7 +77,7 @@ Finally, we can check if the true posterior mean aligns with our approximation ```julia # Visualize a blue density plot of the approximate posterior distribution -density(group(chn, :m);legend=:best, label="Estimated Mean", w=2, c=:blue) +density(group(chn, :m); legend=:best, label="Estimated Mean", w=2, c=:blue) # Visualize the true posterior mean in red vline!([true_posterior_mean]; label="True Mean", c=:red) From 637829c6f194a9efda6ed41f80986574a165f392 Mon Sep 17 00:00:00 2001 From: Colton Botta <39240537+cgbotta@users.noreply.github.com> Date: Wed, 26 Oct 2022 16:22:38 +0100 Subject: [PATCH 16/24] permalink --- tutorials/docs-00-getting-started/00_getting-started.jmd | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tutorials/docs-00-getting-started/00_getting-started.jmd b/tutorials/docs-00-getting-started/00_getting-started.jmd index 33c2bf2e0..d86e28e02 100644 --- a/tutorials/docs-00-getting-started/00_getting-started.jmd +++ b/tutorials/docs-00-getting-started/00_getting-started.jmd @@ -1,6 +1,6 @@ --- title: Getting Started -permalink: /:collection/:name/ +permalink: /docs/using-turing/get-started redirect_from: docs/0-gettingstarted/ weave_options: error : false From d489a1071c147a56ffd58a25f37c6860e3fc6a1c Mon Sep 17 00:00:00 2001 From: Colton Botta <39240537+cgbotta@users.noreply.github.com> Date: Thu, 27 Oct 2022 12:29:13 +0100 Subject: [PATCH 17/24] Added variance calculation --- .../00_getting-started.jmd | 29 +++++++++++++++---- 1 file changed, 24 insertions(+), 5 deletions(-) diff --git a/tutorials/docs-00-getting-started/00_getting-started.jmd b/tutorials/docs-00-getting-started/00_getting-started.jmd index d86e28e02..c119cf580 100644 --- a/tutorials/docs-00-getting-started/00_getting-started.jmd +++ b/tutorials/docs-00-getting-started/00_getting-started.jmd @@ -69,16 +69,35 @@ its updated mean as follows: s² = InverseGamma(2, 3) m = Normal(0, 1) data = [1.5, 2] +x_bar = mean(data) +N = length(data) -true_posterior_mean = (m.σ * m.μ + length(data) * mean(data)) / (m.σ + length(data)) +mean_expectation = (m.σ * m.μ + N * x_bar) / (m.σ + N) ``` -Finally, we can check if the true posterior mean aligns with our approximation +We can also compute the updated variance ```julia -# Visualize a blue density plot of the approximate posterior distribution -density(group(chn, :m); legend=:best, label="Estimated Mean", w=2, c=:blue) +updated_alpha = shape(s²) + (N / 2) +updated_beta = scale(s²) + (1/2) * sum((data[n] - x_bar)^2 for n in 1:N) + (N * m.σ)/(N + m.σ) * ((x_bar)^2)/2 + +variance_expectation = updated_beta / (updated_alpha - 1) +``` + +Finally, we can check if these expectations align with our approximations + +```julia +# Visualize a blue density plot of the mean approximate posterior distribution +density(group(chn, :m); legend=:best, label="Posterior for m (HMC)", w=2, c=:blue) + +# Visualize the true posterior mean in red +vline!([mean_expectation]; label="Posterior for m (Analytical)", c=:red) +``` + +```julia +# Visualize a blue density plot of the variance approximate posterior distribution +density(group(chn, :s²); legend=:best, label="Posterior for s² (HMC)", w=2, c=:blue) # Visualize the true posterior mean in red -vline!([true_posterior_mean]; label="True Mean", c=:red) +vline!([variance_expectation]; label="Posterior for s² (Analytical)", c=:red) ``` From b6a2cd961d5f930326f0f84ef25e64fcdc9a44a5 Mon Sep 17 00:00:00 2001 From: Colton Botta <39240537+cgbotta@users.noreply.github.com> Date: Thu, 27 Oct 2022 12:33:59 +0100 Subject: [PATCH 18/24] formatting --- tutorials/docs-00-getting-started/00_getting-started.jmd | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/tutorials/docs-00-getting-started/00_getting-started.jmd b/tutorials/docs-00-getting-started/00_getting-started.jmd index c119cf580..60d75644b 100644 --- a/tutorials/docs-00-getting-started/00_getting-started.jmd +++ b/tutorials/docs-00-getting-started/00_getting-started.jmd @@ -79,8 +79,10 @@ We can also compute the updated variance ```julia updated_alpha = shape(s²) + (N / 2) -updated_beta = scale(s²) + (1/2) * sum((data[n] - x_bar)^2 for n in 1:N) + (N * m.σ)/(N + m.σ) * ((x_bar)^2)/2 - +updated_beta = + scale(s²) + + (1 / 2) * sum((data[n] - x_bar)^2 for n in 1:N) + + (N * m.σ) / (N + m.σ) * ((x_bar)^2) / 2 variance_expectation = updated_beta / (updated_alpha - 1) ``` From e9c73cc09931ec717ff64839e81ef0de822ff822 Mon Sep 17 00:00:00 2001 From: Colton Botta <39240537+cgbotta@users.noreply.github.com> Date: Thu, 27 Oct 2022 12:36:26 +0100 Subject: [PATCH 19/24] formatting --- tutorials/docs-00-getting-started/00_getting-started.jmd | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tutorials/docs-00-getting-started/00_getting-started.jmd b/tutorials/docs-00-getting-started/00_getting-started.jmd index 60d75644b..470054df3 100644 --- a/tutorials/docs-00-getting-started/00_getting-started.jmd +++ b/tutorials/docs-00-getting-started/00_getting-started.jmd @@ -78,7 +78,7 @@ mean_expectation = (m.σ * m.μ + N * x_bar) / (m.σ + N) We can also compute the updated variance ```julia -updated_alpha = shape(s²) + (N / 2) +updated_alpha = shape(s²) + (N / 2) updated_beta = scale(s²) + (1 / 2) * sum((data[n] - x_bar)^2 for n in 1:N) + From 9dce638a6cefcbd1d7e801923953255a2023fca9 Mon Sep 17 00:00:00 2001 From: Colton Botta <39240537+cgbotta@users.noreply.github.com> Date: Sat, 5 Nov 2022 12:36:29 +0000 Subject: [PATCH 20/24] Updated tutorial posterior visualizations --- .../00_getting-started.jmd | 23 ++++++++----------- 1 file changed, 10 insertions(+), 13 deletions(-) diff --git a/tutorials/docs-00-getting-started/00_getting-started.jmd b/tutorials/docs-00-getting-started/00_getting-started.jmd index 470054df3..be7d85fcd 100644 --- a/tutorials/docs-00-getting-started/00_getting-started.jmd +++ b/tutorials/docs-00-getting-started/00_getting-started.jmd @@ -89,17 +89,14 @@ variance_expectation = updated_beta / (updated_alpha - 1) Finally, we can check if these expectations align with our approximations ```julia -# Visualize a blue density plot of the mean approximate posterior distribution -density(group(chn, :m); legend=:best, label="Posterior for m (HMC)", w=2, c=:blue) - -# Visualize the true posterior mean in red -vline!([mean_expectation]; label="Posterior for m (Analytical)", c=:red) -``` - -```julia -# Visualize a blue density plot of the variance approximate posterior distribution -density(group(chn, :s²); legend=:best, label="Posterior for s² (HMC)", w=2, c=:blue) - -# Visualize the true posterior mean in red -vline!([variance_expectation]; label="Posterior for s² (Analytical)", c=:red) +# Plot the posterior distribtion, calculated analytically +updated_distribution = Normal(mean_expectation, sqrt(variance_expectation)) +samples = rand(updated_distribution, 1000) +density(samples, label="Posterior (Analytical)", w=3) + +# Compare with the approximate posterior distribtion from HMC +approx_mean = mean(chn[:m]) +approx_var = mean(chn[:s²]) +approximated_distribution = Normal(approx_mean, sqrt(approx_var)) +density!(rand(updated_distribution, 1000), label="Posterior (HMC)", w=3) ``` From 6f9c2460ea3c742baf2fab3dfa6fb13a631bcaa9 Mon Sep 17 00:00:00 2001 From: Colton Botta <39240537+cgbotta@users.noreply.github.com> Date: Sat, 5 Nov 2022 12:41:44 +0000 Subject: [PATCH 21/24] Format --- tutorials/docs-00-getting-started/00_getting-started.jmd | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/tutorials/docs-00-getting-started/00_getting-started.jmd b/tutorials/docs-00-getting-started/00_getting-started.jmd index be7d85fcd..cd160e082 100644 --- a/tutorials/docs-00-getting-started/00_getting-started.jmd +++ b/tutorials/docs-00-getting-started/00_getting-started.jmd @@ -92,11 +92,11 @@ Finally, we can check if these expectations align with our approximations # Plot the posterior distribtion, calculated analytically updated_distribution = Normal(mean_expectation, sqrt(variance_expectation)) samples = rand(updated_distribution, 1000) -density(samples, label="Posterior (Analytical)", w=3) +density(samples; label="Posterior (Analytical)", w=3) # Compare with the approximate posterior distribtion from HMC -approx_mean = mean(chn[:m]) -approx_var = mean(chn[:s²]) +approx_mean = mean(chn[:m]) +approx_var = mean(chn[:s²]) approximated_distribution = Normal(approx_mean, sqrt(approx_var)) -density!(rand(updated_distribution, 1000), label="Posterior (HMC)", w=3) +density!(rand(updated_distribution, 1000); label="Posterior (HMC)", w=3) ``` From 8124cac3230b3f7c1835c014889b37c0e760e2af Mon Sep 17 00:00:00 2001 From: Colton Botta <39240537+cgbotta@users.noreply.github.com> Date: Sat, 19 Nov 2022 11:14:07 +0000 Subject: [PATCH 22/24] Calculated density of N-IG properly --- .../00_getting-started.jmd | 29 ++++++++++++------- 1 file changed, 18 insertions(+), 11 deletions(-) diff --git a/tutorials/docs-00-getting-started/00_getting-started.jmd b/tutorials/docs-00-getting-started/00_getting-started.jmd index cd160e082..c436a1139 100644 --- a/tutorials/docs-00-getting-started/00_getting-started.jmd +++ b/tutorials/docs-00-getting-started/00_getting-started.jmd @@ -86,17 +86,24 @@ updated_beta = variance_expectation = updated_beta / (updated_alpha - 1) ``` -Finally, we can check if these expectations align with our approximations +Finally, we can check if these expectations align with our HMC approximations +from earlier. We can compute samples from a normal-inverse gamma following the +equations given [here](https://en.wikipedia.org/wiki/Normal-inverse-gamma_distribution#Generating_normal-inverse-gamma_random_variates). +```julia +function sample_posterior(alpha, beta, mean, lambda, iterations) + samples = [] + for i in 1:iterations + sample_variance = rand(InverseGamma(alpha, beta), 1) + sample_x = rand(Normal(mean, sqrt(sample_variance[1]) / lambda), 1) + sanples = append!(samples, sample_x) + end + return samples +end + +analytical_samples = sample_posterior(updated_alpha, updated_beta, mean_expectation, 2, 1000); +``` ```julia -# Plot the posterior distribtion, calculated analytically -updated_distribution = Normal(mean_expectation, sqrt(variance_expectation)) -samples = rand(updated_distribution, 1000) -density(samples; label="Posterior (Analytical)", w=3) - -# Compare with the approximate posterior distribtion from HMC -approx_mean = mean(chn[:m]) -approx_var = mean(chn[:s²]) -approximated_distribution = Normal(approx_mean, sqrt(approx_var)) -density!(rand(updated_distribution, 1000); label="Posterior (HMC)", w=3) +density(analytical_samples, label="Posterior (Analytical)") +density!(chn[:m], label="Posterior (HMC)") ``` From 38c83a4ee584192429a77ef5a6db8b209766a08d Mon Sep 17 00:00:00 2001 From: Colton Botta <39240537+cgbotta@users.noreply.github.com> Date: Sat, 19 Nov 2022 11:20:26 +0000 Subject: [PATCH 23/24] Format --- .../docs-00-getting-started/00_getting-started.jmd | 11 ++++++----- 1 file changed, 6 insertions(+), 5 deletions(-) diff --git a/tutorials/docs-00-getting-started/00_getting-started.jmd b/tutorials/docs-00-getting-started/00_getting-started.jmd index c436a1139..9edd7083e 100644 --- a/tutorials/docs-00-getting-started/00_getting-started.jmd +++ b/tutorials/docs-00-getting-started/00_getting-started.jmd @@ -72,7 +72,7 @@ data = [1.5, 2] x_bar = mean(data) N = length(data) -mean_expectation = (m.σ * m.μ + N * x_bar) / (m.σ + N) +mean_exp = (m.σ * m.μ + N * x_bar) / (m.σ + N) ``` We can also compute the updated variance @@ -83,12 +83,13 @@ updated_beta = scale(s²) + (1 / 2) * sum((data[n] - x_bar)^2 for n in 1:N) + (N * m.σ) / (N + m.σ) * ((x_bar)^2) / 2 -variance_expectation = updated_beta / (updated_alpha - 1) +variance_exp = updated_beta / (updated_alpha - 1) ``` Finally, we can check if these expectations align with our HMC approximations from earlier. We can compute samples from a normal-inverse gamma following the equations given [here](https://en.wikipedia.org/wiki/Normal-inverse-gamma_distribution#Generating_normal-inverse-gamma_random_variates). + ```julia function sample_posterior(alpha, beta, mean, lambda, iterations) samples = [] @@ -100,10 +101,10 @@ function sample_posterior(alpha, beta, mean, lambda, iterations) return samples end -analytical_samples = sample_posterior(updated_alpha, updated_beta, mean_expectation, 2, 1000); +samples = sample_posterior(updated_alpha, updated_beta, mean_exp, 2, 1000); ``` ```julia -density(analytical_samples, label="Posterior (Analytical)") -density!(chn[:m], label="Posterior (HMC)") +density(analytical_samples; label="Posterior (Analytical)") +density!(chn[:m]; label="Posterior (HMC)") ``` From cfe15eb682ddf7baffdcd2b90da892b0d62abc81 Mon Sep 17 00:00:00 2001 From: Hong Ge <3279477+yebai@users.noreply.github.com> Date: Sat, 19 Nov 2022 14:37:33 +0000 Subject: [PATCH 24/24] Update tutorials/docs-00-getting-started/00_getting-started.jmd --- tutorials/docs-00-getting-started/00_getting-started.jmd | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tutorials/docs-00-getting-started/00_getting-started.jmd b/tutorials/docs-00-getting-started/00_getting-started.jmd index 9edd7083e..0cc30823a 100644 --- a/tutorials/docs-00-getting-started/00_getting-started.jmd +++ b/tutorials/docs-00-getting-started/00_getting-started.jmd @@ -101,7 +101,7 @@ function sample_posterior(alpha, beta, mean, lambda, iterations) return samples end -samples = sample_posterior(updated_alpha, updated_beta, mean_exp, 2, 1000); +analytical_samples = sample_posterior(updated_alpha, updated_beta, mean_exp, 2, 1000); ``` ```julia