replace more filldist with product_distribution

Author: yebai

models/dppl_hier_poisson.jl

@@ -1,4 +1,3 @@
-using LazyArrays
 using Turing: LogPoisson
 
 nd, ns = 5, 10
@@ -19,9 +18,9 @@ lazyarray(f, x) = LazyArray(Base.broadcasted(f, x))
     a0 ~ Normal(0, 10)
     a1 ~ Normal(0, 1)
     a0_sig ~ truncated(Cauchy(0, 1); lower=0)
-    a0s ~ filldist(Normal(0, a0_sig), ns)
+    a0s ~ product_distribution(fill(Normal(0, a0_sig), ns))
     alpha = a0 .+ a0s[idx] .+ a1 * x
-    y ~ arraydist(lazyarray(LogPoisson, alpha))
+    y ~ product_distribution(LogPoisson.(alpha))
 end
 
 model = dppl_hier_poisson(y, x, idx, ns)

models/dppl_high_dim_gauss.jl

@@ -1,5 +1,5 @@
 @model function dppl_high_dim_gauss(D)
-    m ~ filldist(Normal(0, 1), D)
+    m ~ product_distribution(fill(Normal(0, 1), D))
 end
 
 model = dppl_high_dim_gauss(10_000)

models/dppl_lda.jl

@@ -21,8 +21,8 @@ for i in 1:m
 end
 
 @model function dppl_lda(k, m, w, doc, alpha, beta)
-    theta ~ filldist(Dirichlet(alpha), m)
-    phi ~ filldist(Dirichlet(beta), k)
+    theta ~ product_distribution(fill(Dirichlet(alpha), m))
+    phi ~ product_distribution(fill(Dirichlet(beta), k))
     log_phi_dot_theta = log.(phi * theta)
     @addlogprob! sum(log_phi_dot_theta[CartesianIndex.(w, doc)])
 end

models/dppl_naive_bayes.jl

@@ -20,7 +20,7 @@ image_vec = vec(image_subset[:, :])
 labels = labels[1:N]
 
 @model function dppl_naive_bayes(image_vec, labels, C, D)
-    m ~ filldist(Normal(0, 10), C, D)
+    m ~ product_distribution(fill(Normal(0, 10), C, D))
     image_vec ~ MvNormal(vec(m[labels, :]), I)
 end