Merge a2c2439 into a13a865

Project.toml

@@ -10,6 +10,7 @@ ForwardDiff = "f6369f11-7733-5829-9624-2563aa707210"
 LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
 PDMats = "90014a1f-27ba-587c-ab20-58faa44d9150"
 Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
+StatsBase = "2913bbd2-ae8a-5f71-8c99-4fb6c76f3a91"
 StatsFuns = "4c63d2b9-4356-54db-8cca-17b64c39e42c"
 Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
 Tracker = "9f7883ad-71c0-57eb-9f7f-b5c9e6d3789c"

src/multivariate.jl

@@ -27,6 +27,8 @@ struct TuringDiagNormal{Tm<:AbstractVector, Tσ<:AbstractVector} <: ContinuousMu
     σ::Tσ
 end
 
+Distributions.params(d::TuringDiagNormal) = (d.m, d.σ)
+Distributions.length(d::TuringDiagNormal) = length(d.m)
 Distributions.dim(d::TuringDiagNormal) = length(d.m)
 function Distributions.rand(rng::Random.AbstractRNG, d::TuringDiagNormal)
     return d.m .+ d.σ .* randn(rng, dim(d))
@@ -55,6 +57,12 @@ function _logpdf(d::MvNormal, x::Union{Tracker.TrackedVector, Tracker.TrackedMat
     _logpdf(TuringMvNormal(d.μ, getchol(d.Σ)), x)
 end
 
+import StatsBase: entropy
+function entropy(d::TuringDiagNormal)
+    T = eltype(d.σ)
+    return (length(d) * (T(log2π) + one(T)) / 2 + sum(log.(d.σ)))
+end
+
 # zero mean, dense covariance
 MvNormal(A::TrackedMatrix) = MvNormal(zeros(size(A, 1)), A)