STASH norm prior logd

Project.toml

@@ -13,6 +13,7 @@ DistributionsAD = "ced4e74d-a319-5a8a-b0ac-84af2272839c"
 DocStringExtensions = "ffbed154-4ef7-542d-bbb7-c09d3a79fcae"
 FillArrays = "1a297f60-69ca-5386-bcde-b61e274b549b"
 HeterogeneousComputing = "2182be2a-124f-4a91-8389-f06db5907a21"
+IrrationalConstants = "92d709cd-6900-40b7-9082-c6be49f344b6"
 IterativeSolvers = "42fd0dbc-a981-5370-80f2-aaf504508153"
 LineSearches = "d3d80556-e9d4-5f37-9878-2ab0fcc64255"
 LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
@@ -47,6 +48,7 @@ DistributionsAD = "0.6"
 DocStringExtensions = "0.8, 0.9"
 FillArrays = "1.1.1"
 HeterogeneousComputing = "0.2"
+IrrationalConstants = "0.2.2"
 IterativeSolvers = "0.8, 0.9"
 LineSearches = "7"
 LinearAlgebra = "1"

src/MGVI.jl

@@ -22,6 +22,7 @@ using Distributions
 using DistributionsAD
 using FillArrays
 using HeterogeneousComputing: GenContext, allocate_array
+using IrrationalConstants: log2π
 using LineSearches
 using LinearMaps
 using Parameters

src/mgvi_impl.jl

@@ -1,9 +1,15 @@
 # This file is a part of MGVI.jl, licensed under the MIT License (MIT).
 
+function _mv_normal_logdensity(x::AbstractVector{<:Real})
+    T = eltype(x)
+    r = - dot(x, x)/2 - T(length(x)) * T(log2π/2)
+    return r
+end
+
 function posterior_loglike(model, p, data)
     likelihood_value = logdensityof(model(p), data)
-    # normal prior, leaving out `+ length(p)*log2π/2` normalization constant
-    prior_density = - dot(p, p)/2
+    # standard multivariate normal prior:
+    prior_density = _mv_normal_logdensity(p)
     return likelihood_value + prior_density
 end