convinience constructors (#83)

* convinience constructors

* bug

* I --> ones(d)

* Update src/mh-core.jl

Co-authored-by: Tor Erlend Fjelde <[email protected]>

* Update src/mh-core.jl

Co-authored-by: Tor Erlend Fjelde <[email protected]>

* Linear Alg + add previously removed test

* project

* Update src/AdvancedMH.jl

Co-authored-by: David Widmann <[email protected]>

* FillArrays

* more samples for RWMH

* Compat

* Update Project.toml

Co-authored-by: David Widmann <[email protected]>

---------

Co-authored-by: Tor Erlend Fjelde <[email protected]>
Co-authored-by: David Widmann <[email protected]>

Author: 3 people

Project.toml

@@ -5,6 +5,8 @@ version = "0.7.4"
 [deps]
 AbstractMCMC = "80f14c24-f653-4e6a-9b94-39d6b0f70001"
 Distributions = "31c24e10-a181-5473-b8eb-7969acd0382f"
+FillArrays = "1a297f60-69ca-5386-bcde-b61e274b549b"
+LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
 LogDensityProblems = "6fdf6af0-433a-55f7-b3ed-c6c6e0b8df7c"
 Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
 Requires = "ae029012-a4dd-5104-9daa-d747884805df"
@@ -24,6 +26,7 @@ AdvancedMHStructArraysExt = "StructArrays"
 AbstractMCMC = "4"
 DiffResults = "1"
 Distributions = "0.20, 0.21, 0.22, 0.23, 0.24, 0.25"
+FillArrays = "1"
 ForwardDiff = "0.10"
 LogDensityProblems = "2"
 MCMCChains = "5, 6"

src/AdvancedMH.jl

@@ -3,6 +3,8 @@ module AdvancedMH
 # Import the relevant libraries.
 using AbstractMCMC
 using Distributions
+using LinearAlgebra: I
+using FillArrays: Zeros
 
 using LogDensityProblems: LogDensityProblems
 

src/mh-core.jl

@@ -46,7 +46,9 @@ struct MetropolisHastings{D} <: MHSampler
 end
 
 StaticMH(d) = MetropolisHastings(StaticProposal(d))
+StaticMH(d::Int) = MetropolisHastings(StaticProposal(MvNormal(Zeros(d), I)))
 RWMH(d) = MetropolisHastings(RandomWalkProposal(d))
+RWMH(d::Int) = MetropolisHastings(RandomWalkProposal(MvNormal(Zeros(d), I)))
 
 function propose(rng::Random.AbstractRNG, sampler::MHSampler, model::DensityModelOrLogDensityModel)
     return propose(rng, sampler.proposal, model)

test/runtests.jl

@@ -37,32 +37,40 @@ include("util.jl")
         # Set up our sampler with initial parameters.
         spl1 = StaticMH([Normal(0,1), Normal(0, 1)])
         spl2 = StaticMH(MvNormal(zeros(2), I))
+        spl3 = StaticMH(2)
 
         # Sample from the posterior.
         chain1 = sample(model, spl1, 100000; chain_type=StructArray, param_names=["μ", "σ"])
         chain2 = sample(model, spl2, 100000; chain_type=StructArray, param_names=["μ", "σ"])
+        chain3 = sample(model, spl3, 100000; chain_type=StructArray, param_names=["μ", "σ"])
 
         # chn_mean ≈ dist_mean atol=atol_v
         @test mean(chain1.μ) ≈ 0.0 atol=0.1
         @test mean(chain1.σ) ≈ 1.0 atol=0.1
         @test mean(chain2.μ) ≈ 0.0 atol=0.1
         @test mean(chain2.σ) ≈ 1.0 atol=0.1
+        @test mean(chain3.μ) ≈ 0.0 atol=0.1
+        @test mean(chain3.σ) ≈ 1.0 atol=0.1
     end
 
     @testset "RandomWalk" begin
         # Set up our sampler with initial parameters.
         spl1 = RWMH([Normal(0,1), Normal(0, 1)])
         spl2 = RWMH(MvNormal(zeros(2), I))
+        spl3 = RWMH(2)
 
         # Sample from the posterior.
         chain1 = sample(model, spl1, 100000; chain_type=StructArray, param_names=["μ", "σ"])
         chain2 = sample(model, spl2, 100000; chain_type=StructArray, param_names=["μ", "σ"])
+        chain3 = sample(model, spl3, 200000; chain_type=StructArray, param_names=["μ", "σ"])
 
         # chn_mean ≈ dist_mean atol=atol_v
         @test mean(chain1.μ) ≈ 0.0 atol=0.1
         @test mean(chain1.σ) ≈ 1.0 atol=0.1
         @test mean(chain2.μ) ≈ 0.0 atol=0.1
         @test mean(chain2.σ) ≈ 1.0 atol=0.1
+        @test mean(chain3.μ) ≈ 0.0 atol=0.1
+        @test mean(chain3.σ) ≈ 1.0 atol=0.1
     end
 
     @testset "parallel sampling" begin