Rename PriorToGaussian to PriorToNormal

NEWS.md

@@ -14,6 +14,8 @@ Several algorithms have changed their names, but also their role:
     changed (no deprecation for the parameter changes). Tuning and
     sample weighting scheme selection have moved to `TransformedMCMC`.
 
+* `PriorToGaussian` has become `PriorToNormal`.
+
 Partial deprecations are available for the above, a lot of old code should
 run more or less unchanged (with deprecation warnings). Also:
 

docs/src/stable_api.md

@@ -101,7 +101,7 @@ OptimizationAlg
 OrderedResampling
 PosteriorMeasure
 PriorSubstitution
-PriorToGaussian
+PriorToNormal
 PriorToUniform
 RAMTuning
 RandomWalk

examples/dev-internal/test_findmode.jl

@@ -59,7 +59,7 @@ my_function(; nt...)
 
 context = get_batcontext()
 target = posterior
-transformed_density, f_transform = BAT.transform_and_unshape(PriorToGaussian(), target, context)
+transformed_density, f_transform = BAT.transform_and_unshape(PriorToNormal(), target, context)
 inv_trafo = inverse(f_transform)
 initalg = BAT.apply_trafo_to_init(f_transform, InitFromTarget())
 x_init = collect(bat_initval(transformed_density, initalg, context).result)

ext/ahmc_impl/ahmc_sampler_impl.jl

@@ -1,7 +1,7 @@
 # This file is a part of BAT.jl, licensed under the MIT License (MIT).
 
 
-BAT.bat_default(::Type{TransformedMCMC}, ::Val{:pretransform}, proposal::HamiltonianMC) = PriorToGaussian()
+BAT.bat_default(::Type{TransformedMCMC}, ::Val{:pretransform}, proposal::HamiltonianMC) = PriorToNormal()
 
 BAT.bat_default(::Type{TransformedMCMC}, ::Val{:proposal_tuning}, proposal::HamiltonianMC) = StanHMCTuning()
 

src/algodefaults/default_transform_algorithm.jl

@@ -9,10 +9,10 @@ bat_default(::typeof(bat_transform), ::Val{:algorithm}, ::PriorToUniform, ::BATD
 bat_default(::typeof(bat_transform), ::Val{:algorithm}, ::PriorToUniform, ::EvaluatedMeasure) = PriorSubstitution()
 bat_default(::typeof(bat_transform), ::Val{:algorithm}, ::PriorToUniform, ::BATDistMeasure{<:StandardUniformDist}) = IdentityTransformAlgorithm()
 
-bat_default(::typeof(bat_transform), ::Val{:algorithm}, ::PriorToGaussian, ::AbstractPosteriorMeasure) = PriorSubstitution()
-bat_default(::typeof(bat_transform), ::Val{:algorithm}, ::PriorToGaussian, ::BATDistMeasure) = PriorSubstitution()
-bat_default(::typeof(bat_transform), ::Val{:algorithm}, ::PriorToGaussian, ::EvaluatedMeasure) = PriorSubstitution()
-bat_default(::typeof(bat_transform), ::Val{:algorithm}, ::PriorToGaussian, ::BATDistMeasure{<:StandardNormalDist}) = IdentityTransformAlgorithm()
+bat_default(::typeof(bat_transform), ::Val{:algorithm}, ::PriorToNormal, ::AbstractPosteriorMeasure) = PriorSubstitution()
+bat_default(::typeof(bat_transform), ::Val{:algorithm}, ::PriorToNormal, ::BATDistMeasure) = PriorSubstitution()
+bat_default(::typeof(bat_transform), ::Val{:algorithm}, ::PriorToNormal, ::EvaluatedMeasure) = PriorSubstitution()
+bat_default(::typeof(bat_transform), ::Val{:algorithm}, ::PriorToNormal, ::BATDistMeasure{<:StandardNormalDist}) = IdentityTransformAlgorithm()
 
 bat_default(::typeof(bat_transform), ::Val{:algorithm}, ::Function, ::DensitySampleVector) = SampleTransformation()
 bat_default(::typeof(bat_transform), ::Val{:algorithm}, ::AbstractValueShape, ::DensitySampleVector) = SampleTransformation()

src/algotypes/transform_algorithm.jl

@@ -145,7 +145,7 @@ struct ToRealVector <: AbstractTransformTarget end
 export ToRealVector
 
 
-# ToDo: Merge PriorToUniform and PriorToGaussian into PriorTo{Uniform|Normal}.
+# ToDo: Merge PriorToUniform and PriorToNormal into PriorTo{Uniform|Normal}.
 
 """
     struct PriorToUniform <: AbstractTransformTarget
@@ -168,7 +168,7 @@ end
 
 
 """
-    struct PriorToGaussian <: AbstractTransformTarget
+    struct PriorToNormal <: AbstractTransformTarget
 
 Specifies that posterior densities should be transformed in a way that makes
 their pior equivalent to a standard multivariate normal distribution with an
@@ -178,12 +178,12 @@ Constructors:
 
 * ```$(FUNCTIONNAME)()```
 """
-struct PriorToGaussian <: AbstractTransformTarget end
-export PriorToGaussian
+struct PriorToNormal <: AbstractTransformTarget end
+export PriorToNormal
 
-_distmeasure_trafo(target::PriorToGaussian, density::BATDistMeasure) = DistributionTransform(Normal, Distribution(density))
+_distmeasure_trafo(target::PriorToNormal, density::BATDistMeasure) = DistributionTransform(Normal, Distribution(density))
 
-function bat_transform_impl(target::PriorToGaussian, density::BATDistMeasure{<:StandardNormalDist}, algorithm::IdentityTransformAlgorithm, context::BATContext)
+function bat_transform_impl(target::PriorToNormal, density::BATDistMeasure{<:StandardNormalDist}, algorithm::IdentityTransformAlgorithm, context::BATContext)
     (result = density, f_transform = identity)
 end
 
@@ -208,14 +208,14 @@ _get_deep_prior_for_trafo(m::BATDistMeasure) = m
 _get_deep_prior_for_trafo(m::AbstractPosteriorMeasure) = _get_deep_prior_for_trafo(getprior(m))
 
 
-function bat_transform_impl(target::Union{PriorToUniform,PriorToGaussian}, m::AbstractPosteriorMeasure, algorithm::FullMeasureTransform, context::BATContext)
+function bat_transform_impl(target::Union{PriorToUniform,PriorToNormal}, m::AbstractPosteriorMeasure, algorithm::FullMeasureTransform, context::BATContext)
     orig_prior = _get_deep_prior_for_trafo(m)
     f_transform = _distmeasure_trafo(target, orig_prior)
     (result = BATPushFwdMeasure(f_transform, m, KeepRootMeasure()), f_transform = f_transform)
 end
 
 
-function bat_transform_impl(target::Union{PriorToUniform,PriorToGaussian}, m::BATDistMeasure, algorithm::FullMeasureTransform, context::BATContext)
+function bat_transform_impl(target::Union{PriorToUniform,PriorToNormal}, m::BATDistMeasure, algorithm::FullMeasureTransform, context::BATContext)
     f_transform = _distmeasure_trafo(target, m)
     (result = BATPushFwdMeasure(f_transform, m, KeepRootMeasure()), f_transform = f_transform)
 end
@@ -238,14 +238,14 @@ struct PriorSubstitution <: TransformAlgorithm end
 export PriorSubstitution
 
 
-function bat_transform_impl(target::Union{PriorToUniform,PriorToGaussian}, density::BATDistMeasure, algorithm::PriorSubstitution, context::BATContext)
+function bat_transform_impl(target::Union{PriorToUniform,PriorToNormal}, density::BATDistMeasure, algorithm::PriorSubstitution, context::BATContext)
     f_transform = _distmeasure_trafo(target, density)
     transformed_density = BATDistMeasure(f_transform.target_dist)
     (result = transformed_density, f_transform = f_transform)
 end
 
 
-function bat_transform_impl(target::Union{PriorToUniform,PriorToGaussian}, density::AbstractPosteriorMeasure, algorithm::PriorSubstitution, context::BATContext)
+function bat_transform_impl(target::Union{PriorToUniform,PriorToNormal}, density::AbstractPosteriorMeasure, algorithm::PriorSubstitution, context::BATContext)
     orig_prior = getprior(density)
     orig_likelihood = getlikelihood(density)
     new_prior, f_transform = bat_transform_impl(target, orig_prior, algorithm, context)

src/deprecations.jl

@@ -80,3 +80,6 @@ Base.@deprecate MCMCSampling(;
     callback = callback
 )
 export MCMCSampling
+
+
+@deprecate PriorToGaussian() PriorToNormal()

src/extdefs/mgvi_defs.jl

@@ -131,7 +131,7 @@ Fields:
     TR<:AbstractTransformTarget, IA<:InitvalAlgorithm,
     CFG, SD<:MGVISchedule
 } <: AbstractSamplingAlgorithm
-    pretransform::TR = (pkgext(Val(:MGVI)); PriorToGaussian())
+    pretransform::TR = (pkgext(Val(:MGVI)); PriorToNormal())
     init::IA = InitFromTarget()
     nsamples::Int = 10^4
     schedule::SD = FixedMGVISchedule(range(12, nsamples, length = 10))

src/extdefs/optim_defs.jl

@@ -33,7 +33,7 @@ $(TYPEDFIELDS)
     IA<:InitvalAlgorithm
 } <: AbstractModeEstimator
     optalg::ALG = ext_default(pkgext(Val(:Optim)), Val(:DEFAULT_OPTALG))
-    pretransform::TR = PriorToGaussian()
+    pretransform::TR = PriorToNormal()
     init::IA = InitFromTarget()
     maxiters::Int = 1_000
     maxtime::Float64 = NaN

src/extdefs/optimization_defs.jl

@@ -27,7 +27,7 @@ $(TYPEDFIELDS)
     IA<:InitvalAlgorithm
 } <: AbstractModeEstimator
     optalg::ALG = ext_default(pkgext(Val(:Optimization)), Val(:DEFAULT_OPTALG))
-    pretransform::TR = PriorToGaussian()
+    pretransform::TR = PriorToNormal()
     init::IA = InitFromTarget()
     maxiters::Int64 = 1_000
     maxtime::Float64 = NaN

src/integration/bridge_sampling_integration.jl

@@ -17,7 +17,7 @@ Fields:
 $(TYPEDFIELDS)
 """
 @with_kw struct BridgeSampling{TR<:AbstractTransformTarget,ESS<:EffSampleSizeAlgorithm} <: IntegrationAlgorithm
-    pretransform::TR = PriorToGaussian()    
+    pretransform::TR = PriorToNormal()    
     essalg::ESS = EffSampleSizeFromAC()
     strict::Bool = true
     # ToDo: add argument for proposal density generator

src/samplers/mcmc/mcmc_sample.jl

@@ -63,7 +63,7 @@ function MCMCState(samplingalg::TransformedMCMC, target::BATMeasure, id::Integer
 end
 
 
-bat_default(::TransformedMCMC, ::Val{:pretransform}) = PriorToGaussian()
+bat_default(::TransformedMCMC, ::Val{:pretransform}) = PriorToNormal()
 
 bat_default(::TransformedMCMC, ::Val{:nsteps}, ::AbstractTransformTarget, nchains::Integer) = 10^5
 

src/samplers/mcmc/mh_sampler.jl

@@ -25,7 +25,7 @@ struct MHProposalState{Q<:ContinuousUnivariateDistribution} <: MCMCProposalState
 end
 export MHProposalState
 
-bat_default(::Type{TransformedMCMC}, ::Val{:pretransform}, proposal::RandomWalk) = PriorToGaussian()
+bat_default(::Type{TransformedMCMC}, ::Val{:pretransform}, proposal::RandomWalk) = PriorToNormal()
 
 bat_default(::Type{TransformedMCMC}, ::Val{:proposal_tuning}, proposal::RandomWalk) = NoMCMCProposalTuning()
 

test/measures/test_bat_pushfwd_measure.jl

@@ -99,7 +99,7 @@ using Optim
         prior = HierarchicalDistribution(f_secondary, primary_dist)
         likelihood = logfuncdensity(logdensityof(varshape(prior)(MvNormal(Diagonal(fill(1.0, totalndof(varshape(prior))))))))
         m = PosteriorMeasure(likelihood, prior)
-        hmc_samples = bat_sample(m, TransformedMCMC(mcalg = HamiltonianMC(), pretransform = PriorToGaussian(), nsteps = 10^4), context).result
+        hmc_samples = bat_sample(m, TransformedMCMC(mcalg = HamiltonianMC(), pretransform = PriorToNormal(), nsteps = 10^4), context).result
         is_samples = bat_sample(m, PriorImportanceSampler(nsamples = 10^4), context).result
         @test isapprox(mean(unshaped.(hmc_samples)), mean(unshaped.(is_samples)), rtol = 0.1)
         @test isapprox(cov(unshaped.(hmc_samples)), cov(unshaped.(is_samples)), rtol = 0.2)

test/samplers/mcmc/test_hmc.jl

@@ -144,7 +144,7 @@ import AdvancedHMC
         inner_posterior = PosteriorMeasure(likelihood, prior)
         # Test with nested posteriors:
         posterior = PosteriorMeasure(likelihood, inner_posterior)
-        @test BAT.sample_and_verify(posterior, TransformedMCMC(proposal = HamiltonianMC(), proposal_tuning = StanHMCTuning(), pretransform = PriorToGaussian()), prior.dist, context).verified
+        @test BAT.sample_and_verify(posterior, TransformedMCMC(proposal = HamiltonianMC(), proposal_tuning = StanHMCTuning(), pretransform = PriorToNormal()), prior.dist, context).verified
     end
 
     @testset "HMC autodiff" begin

test/samplers/mcmc/test_mh.jl

@@ -128,6 +128,6 @@ using StatsBase, Distributions, StatsBase, ValueShapes, ArraysOfArrays, DensityI
         inner_posterior = PosteriorMeasure(likelihood, prior)
         # Test with nested posteriors:
         posterior = PosteriorMeasure(likelihood, inner_posterior)
-        @test BAT.sample_and_verify(posterior, TransformedMCMC(proposal = RandomWalk(), pretransform = PriorToGaussian()), prior.dist).verified
+        @test BAT.sample_and_verify(posterior, TransformedMCMC(proposal = RandomWalk(), pretransform = PriorToNormal()), prior.dist).verified
     end
 end

test/transforms/test_distribution_transform.jl

@@ -233,7 +233,7 @@ end
     posterior_uniform_prior = @inferred(PosteriorMeasure(logfuncdensity(logdensityof(mvn)), uniform_prior))
     posterior_gaussian_prior = @inferred(PosteriorMeasure(logfuncdensity(logdensityof(mvn)), mvn))
 
-    @test @inferred(bat_transform(PriorToGaussian(), posterior_uniform_prior, context)).result.prior.dist == @inferred(BAT.StandardMvNormal(3))
+    @test @inferred(bat_transform(PriorToNormal(), posterior_uniform_prior, context)).result.prior.dist == @inferred(BAT.StandardMvNormal(3))
     @test @inferred(bat_transform(PriorToUniform(), posterior_gaussian_prior, context)).result.prior.dist == @inferred(BAT.StandardMvUniform(3))
     @test @inferred(bat_transform(DoNotTransform(), posterior_uniform_prior, context)).result.prior.dist == uniform_prior
     pd = @inferred(product_distribution([Uniform() for i in 1:3]))
@@ -243,6 +243,6 @@ end
     # ToDo: Improve comparison for bounds so `.dist` is not required here:
     @inferred(bat_transform(PriorToUniform(), batmeasure(BAT.StandardUvUniform()), context)).result.dist == batmeasure(BAT.StandardUvUniform()).dist
     @inferred(bat_transform(PriorToUniform(), batmeasure(BAT.StandardMvUniform(4)), context)).result.dist == batmeasure(BAT.StandardMvUniform(4)).dist
-    @inferred(bat_transform(PriorToGaussian(), batmeasure(BAT.StandardUvNormal()), context)).result.dist == batmeasure(BAT.StandardUvNormal()).dist
-    @inferred(bat_transform(PriorToGaussian(), batmeasure(BAT.StandardMvNormal(4)), context)).result.dist == batmeasure(BAT.StandardMvNormal(4)).dist
+    @inferred(bat_transform(PriorToNormal(), batmeasure(BAT.StandardUvNormal()), context)).result.dist == batmeasure(BAT.StandardUvNormal()).dist
+    @inferred(bat_transform(PriorToNormal(), batmeasure(BAT.StandardMvNormal(4)), context)).result.dist == batmeasure(BAT.StandardMvNormal(4)).dist
 end