Rethinking SMC

Project.toml

@@ -30,6 +30,7 @@ OptimizationOptimJL = "36348300-93cb-4f02-beb5-3c3902f8871e"
 OrderedCollections = "bac558e1-5e72-5ebc-8fee-abe8a469f55d"
 Printf = "de0858da-6303-5e67-8744-51eddeeeb8d7"
 Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
+Random123 = "74087812-796a-5b5d-8853-05524746bad3"
 Reexport = "189a3867-3050-52da-a836-e630ba90ab69"
 SciMLBase = "0bca4576-84f4-4d90-8ffe-ffa030f20462"
 SpecialFunctions = "276daf66-3868-5448-9aa4-cd146d93841b"
@@ -73,6 +74,7 @@ OptimizationOptimJL = "0.1 - 0.4"
 OrderedCollections = "1"
 Printf = "1"
 Random = "1"
+Random123 = "1.7.1"
 Reexport = "0.2, 1"
 SciMLBase = "2"
 SpecialFunctions = "0.7.2, 0.8, 0.9, 0.10, 1, 2"

src/mcmc/Inference.jl

@@ -23,6 +23,7 @@ using Random: AbstractRNG
 using AbstractMCMC: AbstractModel, AbstractSampler
 using DocStringExtensions: FIELDS, TYPEDEF, TYPEDFIELDS
 using DataStructures: OrderedSet, OrderedDict
+using StatsBase
 
 import ADTypes
 import AbstractMCMC
@@ -35,6 +36,7 @@ import AdvancedPS
 import EllipticalSliceSampling
 import LogDensityProblems
 import Random
+import Random123
 import MCMCChains
 import StatsBase: predict
 
@@ -96,7 +98,7 @@ include("ess.jl")
 include("hmc.jl")
 include("mh.jl")
 include("is.jl")
-include("particle_mcmc.jl")
+include("smc.jl")
 include("sghmc.jl")
 include("emcee.jl")
 include("prior.jl")

src/mcmc/gibbs.jl

@@ -534,11 +534,12 @@ function setparams_varinfo!!(
     return HMCState(params, state.i, state.kernel, hamiltonian, z, state.adaptor, state.ldf)
 end
 
-function setparams_varinfo!!(
-    ::DynamicPPL.Model, ::PG, state::PGState, params::AbstractVarInfo
-)
-    return PGState(params, state.rng)
-end
+# TODO: not sure what to do here... I'll get there eventually
+# function setparams_varinfo!!(
+#     ::DynamicPPL.Model, ::PG, state::PGState, params::AbstractVarInfo
+# )
+#     return PGState(params, state.rng)
+# end
 
 """
     match_linking!!(varinfo_local, prev_state_local, model)