minor updates

src/experimental/ProbabilisticGraphicalModels/ProbabilisticGraphicalModels.jl

@@ -2,34 +2,24 @@ module ProbabilisticGraphicalModels
 
 using BangBang
 using Graphs
+using MetaGraphsNext
 using Distributions
 using JuliaBUGS
-using JuliaBUGS: BUGSGraph, VarName
-using JuliaBUGS.MetaGraphsNext
-
-import Graphs: edges
-import JuliaBUGS.MetaGraphsNext: labels
+using JuliaBUGS: BUGSGraph, VarName, NodeInfo
 
 include("bayesian_network.jl")
 include("conditioning.jl")
 include("functions.jl")
 
-struct NodeInfo{F}
-    is_stochastic::Bool
-    is_observed::Bool
-    node_function_expr::Expr
-    node_function::F
-    node_args::Tuple{Vararg{Symbol}}
-    loop_vars::NamedTuple
-end
-
-export BayesianNetwork, condition, condition!, s
-decondition,
-decondition!,
-ancestral_sampling,
-is_conditionally_independent,
-add_deterministic_vertex!,
-add_stochastic_vertex!,
-add_vertex!,
-translate_BUGSGraph_to_BayesianNetwork
+export BayesianNetwork,
+    condition,
+    condition!,
+    decondition,
+    decondition!,
+    ancestral_sampling,
+    is_conditionally_independent,
+    add_deterministic_vertex!,
+    add_stochastic_vertex!,
+    add_vertex!,
+    translate_BUGSGraph_to_BayesianNetwork
 end

temp.jl

@@ -0,0 +1,38 @@
+using JuliaBUGS
+
+(; model_def, data) = JuliaBUGS.BUGSExamples.rats
+
+model = compile(model_def, data)
+
+ni = model.g[@varname(var"beta.tau")]
+nf = ni.node_function
+
+nf(model.evaluation_env, ())
+
+model.g[@varname(Y[1, 1])] 
+
+vn
+
+using AbstractPPL
+getsym(vn)
+getoptic(vn)
+
+y = rand(5, 5)
+
+getoptic(vn)(y)
+
+AbstractPPL.get(model.evaluation_env, vn)
+
+ex = :(
+function foo(x)
+    return JuliaBUGS.dnorm(x, 1)
+end
+)
+
+Base.eval(:(using JuliaBUGS))
+
+f = Base.eval(ex)
+
+dist = f(1)
+
+f