slim down implementations

Project.toml

@@ -11,7 +11,6 @@ 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]

src/abstractprobprog.jl

@@ -121,28 +121,12 @@ 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)
+    return predict(Random.default_rng(), NamedTuple, model, params)
 end