bayesmith.artifacts.policies

DiagnosticPolicy

Defined in bayesmith.artifacts.policies, line 110.

class DiagnosticPolicy
enabled: bool = True
max_parameters: int = 64
max_matrix_elements: int = 200000
max_prior_parameters: int = 12
required: tuple[str, ...] = ()

Bound dense preflight work; an unperformed check is never a pass.

Bounds count matrix entries and parameter coordinates, not sites.
Required finding codes block execution unless passed. A resolved Jeffreys
assessment (flat or nonflat) also satisfies that check without changing priors.

InitializationPolicy

Defined in bayesmith.artifacts.policies, line 136.

class InitializationPolicy
values: tuple[NamedArray, ...] = ()
masks: tuple[NamedArray, ...] = ()
max_attempts: int = 32
radius: float = 2.0

Complete constrained values independently per chain, before warmup.

A leading ``chain`` dimension means per-chain values; otherwise values
are shared. Boolean masks mark supplied entries. Missing entries are
starting points, not draws claimed to follow a conditional posterior.

StoppingPolicy

Defined in bayesmith.artifacts.policies, line 190.

class StoppingPolicy
mode: str = 'fixed'
min_draws: int = 200
batch_size: int = 200
consecutive: int = 2
ess_min: float | None = None
rhat_max: float | None = None
mcse_mean: float | None = None
max_divergences: int = 0

Fixed budget, or consecutive checks on a continuing warmed chain.

Existing chain diagnostic thresholds always apply. Optional ESS/R-hat
limits may make the criterion stricter. MCSE is an absolute standard
error bound on every scalar posterior mean. Time is a soft batch cap.

ProposalBlockPolicy

Defined in bayesmith.artifacts.policies, line 30.

class ProposalBlockPolicy
names: tuple[str, ...]
method: str
steps: int = 1
iterations: int = 5
scale: float = 1.0
damping: float = 1e-06
max_parameters: int = 64
max_matrix_elements: int = 200000
mh_correction: bool = True

An explicit proposal block with original-target MH enabled by default.

Dense proposal work has an independent budget. The automatic compiler may
also instantiate policies to enlarge small moving-noise linear blocks;
fixed-covariance GCR retains priority and failed enlargement keeps its plan.
Disabling ``mh_correction`` applies finite, supported proposals without
an MH test. Such state-dependent updates are approximate and need not
correspond to any known approximate joint target.

ProposalBlockPolicy.as_options

as_options(self)

Canonical scalar/tuple representation for existing task storage.

proposal_options

Defined in bayesmith.artifacts.policies, line 79.

proposal_options(*blocks: ProposalBlockPolicy)

Return backend options while preserving the existing task schema.

Use ``PosteriorTask(..., backend_options=proposal_options(block1, block2))``.
Other backend options may be concatenated to the returned tuple.