WIP: replace Prior and posterior samples with ParameterDistributions #89
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+ Coverage 79.81% 85.19% +5.37%
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+ Hits 431 466 +35
+ Misses 109 81 -28
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94: Update examples to work with the latest CES code r=bielim a=bielim The goal of this PR is to get all examples synced up with the latest changes in the code base (in particular, PRs #88 and #89) - [x] `Cloudy_example.jl` - [x] `learn_noise.jl` - [x] `plot_GP.jl` In addition, `get_distribution()` (in `ParameterDistribution.jl`) has been modified to return the array of samples when called for `Samples` ( rather than the message "Contains samples only"). `get_distribution` now returns a `Dict` with the parameter names as keys and the corresponding distribution (in the case of `Parameterized` distributions, such as Normal(0.0, 1.0)) or the corresponding samples (in the case of parameters represented by `Samples`) as a parameter_dimension x n_samples array. Co-authored-by: Melanie <[email protected]>
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Purpose
To follow from PR #88 in replacing the prior distributions and posterior distributions with the new type ParameterDistributions, and adding the requisite functionality to make this possible.
Contained in the PR
get_logpdf,get_cov,get_meanand replace implementation in EKP, and MCMC. Note this will also allow us to use prior distributions with block diagonal (i.e not only diagonal) in the MCMC.runtests.jlthat are dependent onPriors.jl, to instead use ParameterDistributionsFuture PR will deal with example cases (not contained in runtests)
Additionally