[dependabot]

Updates the requirements on Turing to permit the latest version.
Updates Turing to 0.48.0

Release notes

Sourced from Turing's releases.

v0.48.0

Turing v0.48.0

Diff since v0.47.4

Breaking changes

The SGLD and SGHMC samplers, which did not support minibatching, have been removed along with PolynomialStepsize (#2890).

MH(var => proposal, ...) and LinkedRW have been removed. Use MH() for prior proposals, or MH(cov_matrix) for a Gaussian random walk over the complete linked parameter vector. In Gibbs, assign MH(cov_matrix) to the target variable block (#2883).

Gibbs now conditions its components with DynamicPPL.condition. GibbsContext and Turing.Inference.make_conditional are removed. Rename isgibbscomponent to supports_gibbs and gibbs_get_raw_values to gibbs_get_parameter_values; the old names are deprecated. Gibbs no longer supports model arguments containing missing: declare the latent variable inside the model and condition on observations instead (#2863).

Every variable reached by a Gibbs model must belong to a component. A component may not change the dimension or existence of a variable owned only by another component; put the variable and whatever decides its shape in one block. Changes to another block's support or distributional form remain unchecked and can make the chain reducible; see the Gibbs docstring (#2863).

Other changes

ESS Gibbs components now use the current conditional prior after another component changes its parameters (#2885).

Gibbs chains now include component-sampler statistics (#2863).

NUTS and HMCDA now accept a NamedTuple or Dict{VarName} initial_params, which HMC already did (#2878).

Emcee now honours chain_type=MCMCChains.Chains, and refuses walkers that start in different parameter layouts instead of failing inside the decode or the stretch proposal (#2879).

estimate_mode now represents indexed bounds using the model's parameter shapes, avoiding growable-array warnings during optimisation (#2888).

estimate_mode now throws on a bound that does not cover a whole variable, where it previously either dropped it and returned the unconstrained mode or failed with a bare DimensionMismatch, and warns on a bound no variable can use rather than ignoring it in silence (#2880).

Prior() now warns that initial_params has no effect instead of discarding it silently (#2881).

Gibbs now rejects an external sampler whose state inherits AbstractMCMC's model-dropping three-argument setparams!! fallback, rather than running with stale model-dependent caches (#2891).

GibbsConditional now distinguishes a ranged tilde statement from multiple element-wise statements when given one conditional distribution (#2891).

Merged pull requests:

• Gibbs: condition components instead of GibbsContext (#2863) (@​yebai)
• hmc: Convert initial_params for NUTS/HMCDA and delete a dead adaptor branch (#2878) (@​yebai)
• emcee: Honour chain_type and refuse walkers in different parameter layouts (#2879) (@​yebai)
• optimisation: Stop silently discarding lb/ub bounds (#2880) (@​yebai)
• prior: Warn that initial_params has no effect (#2881) (@​yebai)
• mh: remove per-variable proposals (#2883) (@​yebai)
• ESS retains stale prior means after Gibbs conditioning (#2885) (@​yebai)
• Reject model-dropping setparams!! in external Gibbs components (#2886) (@​yebai)
• optimisation: eliminate growable constraint warnings (#2888) (@​yebai)
• docs: clarify README (#2889) (@​yebai)
• Remove SGLD and SGHMC (#2890) (@​yebai)
• Fix Gibbs handling of wrapped external samplers and conditional sites (#2891) (@​yebai)

Closed issues:

... (truncated)

Changelog

Sourced from Turing's changelog.

0.48.0

Breaking changes

The SGLD and SGHMC samplers, which did not support minibatching, have been removed along with PolynomialStepsize (#2890).

MH(var => proposal, ...) and LinkedRW have been removed. Use MH() for prior proposals, or MH(cov_matrix) for a Gaussian random walk over the complete linked parameter vector. In Gibbs, assign MH(cov_matrix) to the target variable block (#2883).

Gibbs now conditions its components with DynamicPPL.condition. GibbsContext and Turing.Inference.make_conditional are removed. Rename isgibbscomponent to supports_gibbs and gibbs_get_raw_values to gibbs_get_parameter_values; the old names are deprecated. Gibbs no longer supports model arguments containing missing: declare the latent variable inside the model and condition on observations instead (#2863).

Every variable reached by a Gibbs model must belong to a component. A component may not change the dimension or existence of a variable owned only by another component; put the variable and whatever decides its shape in one block. Changes to another block's support or distributional form remain unchecked and can make the chain reducible; see the Gibbs docstring (#2863).

Other changes

ESS Gibbs components now use the current conditional prior after another component changes its parameters (#2885).

Gibbs chains now include component-sampler statistics (#2863).

NUTS and HMCDA now accept a NamedTuple or Dict{VarName} initial_params, which HMC already did (#2878).

Emcee now honours chain_type=MCMCChains.Chains, and refuses walkers that start in different parameter layouts instead of failing inside the decode or the stretch proposal (#2879).

estimate_mode now represents indexed bounds using the model's parameter shapes, avoiding growable-array warnings during optimisation (#2888).

estimate_mode now throws on a bound that does not cover a whole variable, where it previously either dropped it and returned the unconstrained mode or failed with a bare DimensionMismatch, and warns on a bound no variable can use rather than ignoring it in silence (#2880).

Prior() now warns that initial_params has no effect instead of discarding it silently (#2881).

Gibbs now rejects an external sampler whose state inherits AbstractMCMC's model-dropping three-argument setparams!! fallback, rather than running with stale model-dependent caches (#2891).

GibbsConditional now distinguishes a ranged tilde statement from multiple element-wise statements when given one conditional distribution (#2891).

0.47.4

... (truncated)

Commits

• See full diff in compare view