Add documentation.

docs/pages.jl

@@ -30,6 +30,7 @@ pages = [
             "Custom Generative Functions" => "ref/modeling/custom_gen_fns.md",
         ],
         "Inference Library" => [
+            "Enumerative Inference" => "ref/inference/enumerative.md",
             "Importance Sampling" => "ref/inference/importance.md",
             "Markov Chain Monte Carlo" => "ref/inference/mcmc.md",
             "Particle Filtering & SMC" => "ref/inference/pf.md",

docs/src/ref/inference/enumerative.md

@@ -0,0 +1,18 @@
+# Enumerative Inference
+
+Enumerative inference can be used to compute the exact posterior distribution for a generative model
+with a finite number of discrete random choices, to compute a grid approximation of a continuous 
+posterior density, or to perform stratified sampling by enumerating over discrete random choices and sampling 
+the continuous random choices. This functionality is provided by [`enumerative_inference`](@ref).
+
+```@docs
+enumerative_inference
+```
+
+To construct a rectangular grid of [choice maps](../core/choice_maps.md) and their associated log-volumes to iterate over, use the [`choice_vol_grid`](@ref) function.
+
+```@docs
+choice_vol_grid
+```
+
+When the space of possible choice maps is not rectangular (e.g. some addresses only exist depending on the values of other addresses), iterators over choice maps and log-volumes can be also be manually constructed.

src/inference/enumerative.jl

@@ -17,7 +17,9 @@ Also return an estimate of the log marginal likelihood of the observations (`lml
 All addresses in the `observations` choice map must be sampled by the model when
 given the model arguments. The same constraint applies to choice maps enumerated
 over by `choice_vol_iter`, which must also avoid sharing addresses with the 
-`observations`.
+`observations`. When the choice maps in `choice_vol_iter` do not fully specify
+the values of all unobserved random choices, the unspecified choices are sampled
+from the internal proposal distribution of the model.
 """
 function enumerative_inference(
     model::GenerativeFunction{T,U}, model_args::Tuple,