Pointpriors

Project.toml

@@ -1,6 +1,6 @@
 name = "DynamicPPL"
 uuid = "366bfd00-2699-11ea-058f-f148b4cae6d8"
-version = "0.29.1"
+version = "0.29.2"
 
 [deps]
 ADTypes = "47edcb42-4c32-4615-8424-f2b9edc5f35b"

docs/src/api.md

@@ -124,10 +124,14 @@ Return values of the model function for a collection of samples can be obtained
 generated_quantities
 ```
 
-For a chain of samples, one can compute the pointwise log-likelihoods of each observed random variable with [`pointwise_loglikelihoods`](@ref).
+For a chain of samples, one can compute the pointwise log-likelihoods of each observed random variable with [`pointwise_loglikelihoods`](@ref). Similarly, the log-densities of the priors using
+[`pointwise_prior_logdensities`](@ref) or both, i.e. all variables, using
+[`pointwise_logdensities`](@ref).
 
 ```@docs
+pointwise_logdensities
 pointwise_loglikelihoods
+pointwise_prior_logdensities
 ```
 
 For converting a chain into a format that can more easily be fed into a `Model` again, for example using `condition`, you can use [`value_iterator_from_chain`](@ref).

src/DynamicPPL.jl

@@ -115,6 +115,8 @@ export AbstractVarInfo,
     # Convenience functions
     logprior,
     logjoint,
+    pointwise_prior_logdensities,
+    pointwise_logdensities,
     pointwise_loglikelihoods,
     condition,
     decondition,
@@ -181,7 +183,7 @@ include("varinfo.jl")
 include("simple_varinfo.jl")
 include("context_implementations.jl")
 include("compiler.jl")
-include("loglikelihoods.jl")
+include("pointwise_logdensities.jl")
 include("submodel_macro.jl")
 include("test_utils.jl")
 include("transforming.jl")