Update Turing requirement from 0.45, 0.46 to 0.45, 0.46, 0.47 in /docs in the all-julia-packages group across 1 directory

[dependabot]

Updates the requirements on Turing to permit the latest version.
Updates Turing to 0.47.1

Release notes

Sourced from Turing's releases.

v0.47.1

Turing v0.47.1

Diff since v0.47.0

setprogress! is now a re-export of AbstractMCMC.setprogress!, so Turing.setprogress!(false) keeps working and there is a single progress logging switch instead of two. The internal Turing.PROGRESS flag is gone, its state lives in AbstractMCMC.PROGRESS.

Changelog

Sourced from Turing's changelog.

0.47.1

setprogress! is now a re-export of AbstractMCMC.setprogress!, so Turing.setprogress!(false) keeps working and there is a single progress logging switch instead of two. The internal Turing.PROGRESS flag is gone, its state lives in AbstractMCMC.PROGRESS.

0.47.0

Breaking changes

Particle MCMC (SMC and PG)

SMC and PG / CSMC have been reimplemented natively and no longer depend on AdvancedPS.

Resampling schemes are now types rather than functions: StratifiedResampler(), SystematicResampler(), and MultinomialResampler() (in Turing.Inference), optionally wrapped in ESSThresholdResampler(threshold, scheme) to resample only when the effective sample size falls below threshold * nparticles. For example SMC(Turing.Inference.SystematicResampler()), SMC(0.5), or PG(10, Turing.Inference.MultinomialResampler(), 0.5). The old function-based API (resample_systematic, AdvancedPS.ResampleWithESSThreshold, ...) is gone.

The default scheme is now stratified rather than systematic: it stays consistent as the number of particles grows, which systematic does not. The selected scheme applies to unconditional sweeps only; PG / CSMC draw a conditional sweep's ancestors from the categorical over the weights. Exact draws may therefore differ from previous releases, but remain statistically consistent (the same target distribution).

Chain statistics have changed: chain[:logevidence] is now chain[:log_normalizing_constant], and SMC's per-particle weight is gone, since the returned particles are now equal-weight.

SMC no longer has a sampler state, because it runs one sweep rather than an MCMC loop: save_state and initial_state are now warned about and ignored, so loadstate has nothing to return for an SMC chain. PG / CSMC are unaffected.

The rewrite also brings:

• Reproducibility. Internal seeds are derived through a counter-based (Philox) generator, so a fixed user seed gives the same draws on every Julia version and platform. Previously, results could drift between Julia versions even under a StableRNG (TuringLang/Turing.jl#2781).
• Parallelism within a sweep. SMC(; multithreaded=true) / PG(n; multithreaded=true) spread that sweep's particles across threads without changing the results; start Julia with multiple threads (e.g. julia -t auto) for this to take effect. It is independent of MCMCThreads() / MCMCDistributed(), which parallelise whole chains and work with SMC/PG as with any other sampler.
• Equal-weight draws. SMC resamples once at the end of the sweep, so mean(chain[...]) and other summaries need no weighting.
• A degeneracy diagnostic. SMC chains carry ess_per_step, the effective sample size after each filtering step; one entry per likelihood term, so an @addlogprob! adds one alongside the observations. MCMCChains exposes the entries as ess_per_step[1], ess_per_step[2], and so on.
• For SMC, exp(log_normalizing_constant) is an unbiased estimator of the marginal likelihood p(y) under the usual particle-filter assumptions. For PG / CSMC it is biased and must not be used for model comparison (see the PG docstring).

0.46.1

Fixed a bug, present since v0.41.0, that biased PG / CSMC posteriors, whether sampled on their own or as a Gibbs component.

0.46.0

Breaking changes

DynamicPPL 0.42

Turing.jl v0.46 brings with it all the underlying changes in DynamicPPL 0.42. Most notably, gradient preparation and evaluation now go through AbstractPPL's prepare / value_and_gradient!! interface. This is an internal change and does not affect sampling results. Please see the DynamicPPL changelog for full details, and the AdvancedVI section below for the user-facing changes in this release.

AdvancedVI 0.7

Turing.jl v0.46 also brings in the changes in AdvancedVI 0.7.

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[github-actions]

SliceSampling.jl documentation for PR #69 is available at:
https://TuringLang.github.io/SliceSampling.jl/previews/PR69/