Merge b72a963 into cc6439e

Project.toml

@@ -11,11 +11,14 @@ Accessors = "7d9f7c33-5ae7-4f3b-8dc6-eff91059b697"
 DensityInterface = "b429d917-457f-4dbc-8f4c-0cc954292b1d"
 JSON = "682c06a0-de6a-54ab-a142-c8b1cf79cde6"
 Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
+SparseArrays = "2f01184e-e22b-5df5-ae63-d93ebab69eaf"
+StatsBase = "2913bbd2-ae8a-5f71-8c99-4fb6c76f3a91"
 
 [compat]
 AbstractMCMC = "2, 3, 4, 5"
 Accessors = "0.1"
 DensityInterface = "0.4"
 JSON = "0.19 - 0.21"
 Random = "1.6"
+StatsBase = "0.32, 0.33"
 julia = "~1.6.6, 1.7.3"

src/abstractprobprog.jl

@@ -1,6 +1,7 @@
 using AbstractMCMC
 using DensityInterface
 using Random
+using StatsBase
 
 
 """
@@ -121,3 +122,28 @@ end
 function Base.rand(model::AbstractProbabilisticProgram)
     return rand(Random.default_rng(), NamedTuple, model)
 end
+
+"""
+    predict(
+        [rng::AbstractRNG=Random.default_rng(),]
+        [T=NamedTuple,]
+        model::AbstractProbabilisticProgram,
+        params,
+    ) -> T
+
+Draw a sample from the predictive distribution specified by `model` with its parameters fixed to `params`.
+
+The sample will be returned as format specified by `T`.
+"""
+function StatsBase.predict(rng::AbstractRNG, T::Type, model::AbstractProbabilisticProgram, params)
+    return rand(rng, T, fix(model, params))
+end
+function StatsBase.predict(T::Type, model::AbstractProbabilisticProgram, params)
+    return StatsBase.predict(Random.default_rng(), T, model, params)
+end
+function StatsBase.predict(model::AbstractProbabilisticProgram, params)
+    return StatsBase.predict(NamedTuple, model, params)
+end
+function StatsBase.predict(rng::AbstractRNG, T::Type, model::AbstractProbabilisticProgram, params)
+    return StatsBase.predict(rng, NamedTuple, model, params)
+end