Added model which calls C

main.jl

@@ -76,6 +76,7 @@ end
 # although it's hardly a big deal.
 @include_model "Base Julia features" "control_flow"
 @include_model "Base Julia features" "multithreaded"
+@include_model "Base Julia features" "call_C"
 @include_model "Core Turing syntax" "broadcast_macro"
 @include_model "Core Turing syntax" "dot_assume"
 @include_model "Core Turing syntax" "dot_observe"
@@ -114,6 +115,7 @@ end
 @include_model "Effect of model size" "n500"
 @include_model "PosteriorDB" "pdb_eight_schools_centered"
 @include_model "PosteriorDB" "pdb_eight_schools_noncentered"
+@include_model "Miscellaneous features" "metabayesian_MH"
 
 # The entry point to this script itself begins here
 if ARGS == ["--list-model-keys"]

models/call_C.jl

@@ -0,0 +1,10 @@
+@model function call_C(y = 0.0)
+    x ~ Normal(0, 1)
+
+    # Call C library abs function
+    x_abs = @ccall fabs(x::Cdouble)::Cdouble
+
+    y ~ Normal(0, x_abs)
+end
+
+model = call_C()

models/metabayesian_MH.jl

@@ -0,0 +1,48 @@
+#=
+This is a "meta-Bayesian" model, where the generative model includes an inversion of a different generative model.
+These types of models are common in cognitive modelling, where systems of interest (e.g. human subjects) are thought to use Bayesian inference to navigate their environment.
+Here we use a Metropolis-Hasting sampler implemented with Turing as the inversion of the inner "subjective" model.
+=#
+
+# Inner model function
+@model function inner_model(observation, prior_μ = 0, prior_σ = 1)
+
+    # The innter model's prior
+    mean ~ Normal(prior_μ, prior_σ)
+
+    # The inner model's likelihood
+    observation ~ Normal(mean, 1)
+end
+
+# Outer model function
+@model function metabayesian_MH(observation, action, inner_sampler = MH(), inner_n_samples = 20)
+
+    ### Sample parameters for the inner inference and response ###
+
+    #The inner model's prior's sufficient statistics
+    subj_prior_μ ~ Normal(0, 1)
+    subj_prior_σ = 1.0 
+
+    # #Inverse temperature for actions
+    β ~ 1
+
+    ### "Perceptual inference": running the inner model ###
+
+    #Condition the inner model
+    inner_m = inner_model(observation, subj_prior_μ, subj_prior_σ)
+
+    #Run the inner Bayesian inference
+    chns = sample(inner_m, inner_sampler, inner_n_samples, progress = false)
+
+    #Extract subjective point estimate
+    subj_mean_expectationₜ = mean(chns[:mean])
+
+
+    ### "Response model": picking an action ###
+
+    #The action is a Gaussian-noise report of the subjective point estimate
+    action ~ Normal(subj_mean_expectationₜ, β)
+
+end
+
+model = metabayesian_MH(0.0, 1.0)