torfjelde

julia> using Turing, TuringBenchmarking, ADTypes

julia> @model function f_model(n)
           μ ~ truncated(Normal(), lower=0)
           σ² ~ InverseGamma(2, 3)
           x ~ filldist(truncated(Normal(μ, sqrt(σ²)); lower=0), n)
       end

f_model (generic function with 2 methods)

torfjelde

# This file is machine-generated - editing it directly is not advised

julia_version = "1.10.0"
manifest_format = "2.0"
project_hash = "a0590676db40552635a5877095b47d6a64ec5413"

[[deps.ADTypes]]
git-tree-sha1 = "41c37aa88889c171f1300ceac1313c06e891d245"
uuid = "47edcb42-4c32-4615-8424-f2b9edc5f35b"
version = "0.2.6"

torfjelde

Setup

Before you do anything, you'll need to install the following pieces of software:

• pandoc for converting Markdown to HTML.
• entr for automagic conversion of Markdown to HTML upon changes to underlying Markdown.
• browser-sync for automagic refresh upon changes to underlying HTML.

And you need a web browser. Hopefully one is available to you.

The next step is to get yourself a local copy of MathJax v3, which can be achieved by executing

torfjelde

GC error (probable corruption) :
Allocations: 871149 (Pool: 870842; Big: 307); GC: 0

!!! ERROR in jl_ -- ABORTING !!!
0x7f59051d5010: Root object: 0x7f59435fc010 :: 0x7f593ed1a300 (bits: 1)
        of type Task
0x7f59051d5028: Root object: 0x7f593e6e9cb0 :: 0x7f593ed1c740 (bits: 3)
        of type Module
0x7f59051d5040: Root object: 0x7f593ebffff0 :: 0x7f593ed1a110 (bits: 3)
        of type Array{Any, 1}

torfjelde

julia> using PosteriorDB, StanDistributions, Turing, BridgeStan, LinearAlgebra

julia> # Necessary overloads to make it work with Turing.
       function DynamicPPL.init(rng, dist::StanDistribution, ::DynamicPPL.SampleFromPrior)
           # `init` uses `rand` by default but this is not supported for `StanDistribution`.
           return BridgeStan.param_constrain(dist.model, randn(rng, length(dist)))
       end

julia> function DynamicPPL.with_logabsdet_jacobian_and_reconstruct(f, dist::StanDistribution, x)
           # HACK: This is cheating.

torfjelde

using DynamicPPL: OrderedDict, SamplingContext, AbstractContext, IsParent, VarName, Distribution, evaluate!!, VarInfo
import DynamicPPL: tilde_assume, dot_tilde_assume, childcontext, setchildcontext, NodeTrait

Base.@kwdef struct PriorExtractorContext{D,Ctx} <: AbstractContext
    priors::D=OrderedDict{VarName,Any}()
    context::Ctx=SamplingContext()
end

NodeTrait(::PriorExtractorContext) = IsParent()
childcontext(context::PriorExtractorContext) = context.context

torfjelde

#!/usr/bin/env julia
using ArgParse

# Scenarios:
# 1. Project is specified, but no files => watch project + run `runtests.jl`.
# 2. Project is specified, and files => watch project + run files.

# Command line arguments.
s = ArgParseSettings()
@add_arg_table s begin

torfjelde

using DynamicPPL

varnames_to_ranges(model::DynamicPPL.Model) = varnames_to_ranges(DynamicPPL.VarInfo(model))
varnames_to_ranges(varinfo::DynamicPPL.UntypedVarInfo) = varnames_to_ranges(varinfo.metadata)
function varnames_to_ranges(varinfo::DynamicPPL.TypedVarInfo)
    offset = 0
    dicts = map(varinfo.metadata) do md
        vns2ranges = varnames_to_ranges(md)
        vals = collect(values(vns2ranges))
        vals_offset = map(r -> offset .+ r, vals)

torfjelde

julia> include("nested_samplers.jl")

julia> @model function demo()
           m ~ Normal()
           x ~ Normal(m, 1)
           y ~ Normal(x, 1)
           return (; m, x, y)
       end
demo (generic function with 2 methods)

torfjelde

# Use packages to ensure that we trigger Requires.jl.
using Zygote: Zygote
using ReverseDiff: ReverseDiff
using ForwardDiff: ForwardDiff
using Tracker: Tracker
using Memoization: Memoization # used for ReverseDiff.jl cache.

using Turing.Core: ForwardDiffAD, ReverseDiffAD, TrackerAD, ZygoteAD, CHUNKSIZE

const DEFAULT_ADBACKENDS = [