diff --git a/_quarto.yml b/_quarto.yml index 422001106..c3910ddc4 100644 --- a/_quarto.yml +++ b/_quarto.yml @@ -17,8 +17,6 @@ website: navbar: logo: "assets/images/turing-logo.svg" logo-href: https://turinglang.org/ - #background: "#073c44" - #foreground: "#ffffff" left: - text: Get Started href: https://turinglang.org/docs/tutorials/docs-00-getting-started/ @@ -63,6 +61,7 @@ format: theme: light: [cosmo, theming/light.scss] dark: [cosmo, theming/dark.scss] + highlight-style: github toc: true smooth-scroll: true code-overflow: wrap diff --git a/index.qmd b/index.qmd index 36c9ef99f..c95c2cdd0 100644 --- a/index.qmd +++ b/index.qmd @@ -8,33 +8,96 @@ description: | Turing.jl is a probabilistic programming language and Bayesian modelling framework for the Julia programming language. --- -
-Three normal probability distributions -Turing.jl -Bayesian inference with probabilistic programming -
-[Get Started](https://turinglang.org/docs/tutorials/docs-00-getting-started/){.button--fill .btn} - - -[API reference](/library/){.button .btn} -
-
-
-
-
Intuitive
- Turing models are easy to write and communicate — syntax is close to mathematical notations. +```{=html} +
+
+ Three normal probability distributions + + Turing.jl + + + Bayesian inference with probabilistic programming + +
+ + Tutorials + + + Ecosystem + + + View on GitHub + +
- -
-
General-purpose
- Turing supports models with discrete parameters and stochastic control flow. +
+
+
+ Intuitive +
+ Turing models are easy to write and communicate — syntax is close to mathematical notations. +
+
+
+ General-purpose +
+ Turing supports models with discrete parameters and stochastic control flow. +
+
+
+ Modular & composable +
+ Turing is modular, written entirely in Julia, and is interoperable with the powerful Julia ecosystem. +
- -
-
Modular & composable
- Turing is modular, written entirely in Julia, and is interoperable with the powerful Julia ecosystem. +
+``` +
+
+
+
+ Hello, World in Turing
+ Some text about how easy it is to [get going](https://turinglang.org/docs/tutorials/00-introduction/). +
+
+```julia +@model function coinflip(; N::Int) + # Prior belief about the probability of heads + p ~ Beta(1, 1) + + # Heads or tails of a coin are drawn from `N` + # Bernoulli distributions with success rate `p` + y ~ filldist(Bernoulli(p), N) + + return y +end; +``` +
+
+
+```julia +@model function putting_model(d, n; jitter=1e-4) + v ~ Gamma(2, 1) + l ~ Gamma(4, 1) + f = GP(v * with_lengthscale(SEKernel(), l)) + f_latent ~ f(d, jitter) + binomials = Binomial.(n, logistic.(f_latent)) + y ~ product_distribution(binomials) + return (fx=f(d, jitter), f_latent=f_latent, y=y) +end +``` +
+
+
+ Goodbye, World in Turing +
+ Some text about how easy it is to interface with external packages like AbstractGPs. Learn more about modelling [Gaussian Processes](https://turinglang.org/docs/tutorials/15-gaussian-processes/) with Turing.jl. +
+
+
+