Use linked varinfo by default

Author: penelopeysm

Project.toml

@@ -23,7 +23,6 @@ MacroTools = "1914dd2f-81c6-5fcd-8719-6d5c9610ff09"
 OrderedCollections = "bac558e1-5e72-5ebc-8fee-abe8a469f55d"
 Printf = "de0858da-6303-5e67-8744-51eddeeeb8d7"
 Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
-Requires = "ae029012-a4dd-5104-9daa-d747884805df"
 Statistics = "10745b16-79ce-11e8-11f9-7d13ad32a3b2"
 Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
 
@@ -42,8 +41,8 @@ DynamicPPLChainRulesCoreExt = ["ChainRulesCore"]
 DynamicPPLEnzymeCoreExt = ["EnzymeCore"]
 DynamicPPLForwardDiffExt = ["ForwardDiff"]
 DynamicPPLJETExt = ["JET"]
-DynamicPPLMarginalLogDensitiesExt = ["MarginalLogDensities"]
 DynamicPPLMCMCChainsExt = ["MCMCChains"]
+DynamicPPLMarginalLogDensitiesExt = ["MarginalLogDensities"]
 DynamicPPLMooncakeExt = ["Mooncake"]
 
 [compat]
@@ -67,14 +66,13 @@ JET = "0.9, 0.10"
 KernelAbstractions = "0.9.33"
 LinearAlgebra = "1.6"
 LogDensityProblems = "2"
-MarginalLogDensities = "0.4.1"
 MCMCChains = "6, 7"
 MacroTools = "0.5.6"
+MarginalLogDensities = "0.4.1"
 Mooncake = "0.4.147"
 OrderedCollections = "1"
 Printf = "1.10"
 Random = "1.6"
-Requires = "1"
 Statistics = "1"
 Test = "1.6"
 julia = "1.10.8"

ext/DynamicPPLMarginalLogDensitiesExt.jl

@@ -9,8 +9,8 @@ _to_varname(n::VarName) = n
 """
     marginalize(
         model::DynamicPPL.Model,
-        varinfo::DynamicPPL.AbstractVarInfo=VarInfo(model),
         varnames::AbstractVector{<:Union{Symbol,<:VarName}},
+        varinfo::DynamicPPL.AbstractVarInfo=link(VarInfo(model), model),
         getlogprob=DynamicPPL.getlogjoint,
         method::MarginalLogDensities.AbstractMarginalizer=MarginalLogDensities.LaplaceApprox();
         kwargs...,
@@ -23,6 +23,10 @@ log-density of the given `model`, after marginalizing out the variables specifie
 The resulting object can be called with a vector of parameter values to compute the marginal
 log-density.
 
+You can specify the `varinfo` to use for the model. By default we use a linked `VarInfo`,
+meaning that the resulting log-density function accepts parameters that have been
+transformed to unconstrained space.
+
 The `getlogprob` argument can be used to specify which kind of marginal log-density to
 compute. Its default value is `DynamicPPL.getlogjoint` which returns the marginal log-joint
 probability.
@@ -55,7 +59,7 @@ julia> logpdf(Normal(2.0), 1.0)
 function DynamicPPL.marginalize(
     model::DynamicPPL.Model,
     varnames::AbstractVector{<:Union{Symbol,<:VarName}},
-    varinfo::DynamicPPL.AbstractVarInfo=DynamicPPL.VarInfo(model),
+    varinfo::DynamicPPL.AbstractVarInfo=DynamicPPL.link(DynamicPPL.VarInfo(model), model),
     getlogprob::Function=DynamicPPL.getlogjoint,
     method::MarginalLogDensities.AbstractMarginalizer=MarginalLogDensities.LaplaceApprox();
     kwargs...,