reinstate test/conjugates_mvnormal.jl

src/Distributions.jl

@@ -90,7 +90,7 @@ export
     MultivariateNormal,
     MvNormal,
     MvNormalCanon,
-    MvNormalKnownSigma,
+    MvNormalKnownCov,
     MvTDist,
     NegativeBinomial,
     NoncentralBeta,

src/conjugates/mvnormal.jl

@@ -7,21 +7,21 @@ function posterior_canon(prior::MvNormal, ss::MvNormalKnownCovStats)
     μ0 = prior.μ
     invΣp = pdadd(invΣ0, ss.invΣ, ss.tw)
     h = add!(invΣ0 * μ0, ss.invΣ * ss.sx)
-	return GenericMvNormalCanon(h, invΣp)
+	return MvNormalCanon(h, invΣp)
 end
 
 function posterior_canon{Pri<:MvNormal,Cov<:AbstractPDMat}(
     prior::(Pri, Cov), 
-    G::Type{MvNormal{Cov}}, 
+    G::Type{MvNormal}, 
     x::Matrix) 
 
 	μpri::Pri, Σ::Cov = prior
-	posterior_canon(μpri, suffstats(MvNormalKnownSigma{Cov}(Σ), x))
+	posterior_canon(μpri, suffstats(MvNormalKnownCov{Cov}(Σ), x))
 end
 
 function posterior_canon{Pri<:MvNormal,Cov<:AbstractPDMat}(
     prior::(Pri, Cov), 
-    G::Type{MvNormal{Cov}}, 
+    G::Type{MvNormal}, 
     x::Matrix, w::Array{Float64}) 
 
     μpri::Pri, Σ::Cov = prior
@@ -30,22 +30,22 @@ end
 
 function posterior{Pri<:MvNormal,Cov<:AbstractPDMat}(
     prior::(Pri, Cov), 
-    G::Type{MvNormal{Cov}}, 
+    G::Type{MvNormal}, 
     x::Matrix) 
 
-    convert(Pri, posterior_canon(prior, G, x))
+    meanform(posterior_canon(prior, G, x))
 end
 
 function posterior{Pri<:MvNormal,Cov<:AbstractPDMat}(
     prior::(Pri, Cov), 
-    G::Type{MvNormal{Cov}}, 
+    G::Type{MvNormal}, 
     x::Matrix, w::Array{Float64}) 
 
-    convert(Pri, posterior_canon(prior, G, x, w))
+    meanform(posterior_canon(prior, G, x, w))
 end
 
 function complete{Pri<:MvNormal,Cov<:AbstractPDMat}(
-    G::Type{MvNormal{Cov}},
+    G::Type{MvNormal},
     pri::(Pri, Cov), 
     μ::Vector{Float64})
 

src/multivariate/mvnormal.jl

@@ -142,6 +142,9 @@ immutable MvNormalKnownCov{Cov<:AbstractPDMat}
 end
 
 MvNormalKnownCov{Cov<:AbstractPDMat}(C::Cov) = MvNormalKnownCov{Cov}(C)
+MvNormalKnownCov(d::Int, σ::Real) = MvNormalKnownCov(ScalMat(d, abs2(float64(σ))))
+MvNormalKnownCov(σ::Vector{Float64}) = MvNormalKnownCov(PDiagMat(abs2(σ)))
+MvNormalKnownCov(Σ::Matrix{Float64}) = MvNormalKnownCov(PDMat(Σ))
 
 length(g::MvNormalKnownCov) = dim(g.Σ)
 

test/conjugates_mvnormal.jl

@@ -12,8 +12,8 @@ w = rand(n)
 Xw = X * diagm(w)
 
 Sigma = 0.75 * eye(p) + fill(0.25, 4, 4)
-ss = suffstats(MvNormalKnownSigma(Sigma), X)
-ssw = suffstats(MvNormalKnownSigma(Sigma), X, w)
+ss = suffstats(MvNormalKnownCov(Sigma), X)
+ssw = suffstats(MvNormalKnownCov(Sigma), X, w)
 
 s_t = sum(X, 2)
 ws_t = sum(Xw, 2)
@@ -38,17 +38,17 @@ X = rand(MultivariateNormal(mu_true, Sig_true), n)
 
 pri = MultivariateNormal(mu0, Sig0)
 
-post = posterior((pri, Sig_true), MultivariateNormal, X)
-@test isa(post, MultivariateNormal)
+post = posterior((pri, Sig_true), MvNormal, X)
+@test isa(post, FullNormal)
 
 @test_approx_eq post.μ inv(inv(Sig0) + n*inv(Sig_true))*(n*inv(Sig_true)*mean(X,2) + inv(Sig0)*mu0)
 @test_approx_eq post.Σ.mat inv(inv(Sig0) + n*inv(Sig_true))
 
 # posterior_sample
 
-ps = posterior_randmodel((pri, Sig_true), MultivariateNormal, X)
+ps = posterior_randmodel((pri, Sig_true), MvNormal, X)
 
-@test isa(ps, MultivariateNormal)
+@test isa(ps, FullNormal)
 @test insupport(ps, ps.μ)
 @test insupport(InverseWishart, ps.Σ.mat)
 
@@ -89,7 +89,7 @@ mu_true = [2., 2.]
 Lam_true = eye(2)
 Lam_true[1,2] = Lam_true[2,1] = 0.25
 
-X = rand(MultivariateNormal(mu_true, inv(Lam_true)), n)
+X = rand(MvNormal(mu_true, inv(Lam_true)), n)
 Xbar = mean(X,2)
 Xm = X .- Xbar
 

test/runtests.jl

@@ -16,7 +16,7 @@ tests = [
     "compoundvariate",
     "conjugates",
     "conjugates_normal",
-    # "conjugates_mvnormal",
+    "conjugates_mvnormal",
     "wishart",
     "mixture",
     "gradlogpdf"]