mixle.inference.priors module¶

Prior specifications for declaration-backed MAP fitting.

The classes in this module are lightweight, serializable descriptions of common priors. They intentionally do not depend on NumPy, Torch, or a concrete compute engine; fitting code turns them into backend tensors at objective time.

class BetaPrior(alpha, beta, parameter=None)[source]

Bases: object

Beta prior for unit-interval parameters.

Parameters:

alpha (float)
beta (float)
parameter (str | None)

alpha: float

beta: float

parameter: str | None = None

as_dict()[source]

Return the normalized Beta prior payload.

Return type:: dict

class ConditionalPrior(conditions, default=None, given=None)[source]

Bases: object

Per-key, default, and given priors for a ConditionalDistribution.

Parameters:

conditions (Mapping[Any, Any])
default (Any | None)
given (Any | None)

conditions: Mapping[Any, Any]

default: Any | None = None

given: Any | None = None

as_dict()[source]

Return keyed/default/given priors as a plain payload.

Return type:: dict

class CompositePrior(children)[source]

Bases: object

Child priors for a CompositeDistribution.

Parameters:: children (Sequence[Any])

children: Sequence[Any]

as_dict()[source]

Return child priors as a plain composite-prior payload.

Return type:: dict

class DirichletPrior(alpha)[source]

Bases: object

Dirichlet prior for simplex-valued parameters.

Parameters:: alpha (Any)

alpha: Any

as_dict()[source]

Return the normalized Dirichlet prior payload.

Return type:: dict

class GammaPrior(shape, rate, parameter=None)[source]

Bases: object

Gamma prior for positive scalar parameters or ordered-bound deltas.

Parameters:

shape (float)
rate (float)
parameter (str | None)

shape: float

rate: float

parameter: str | None = None

as_dict()[source]

Return the normalized Gamma prior payload.

Return type:: dict

class MarkovChainPrior(initial=None, transitions=None, length=None)[source]

Bases: object

Initial, transition-row, and length priors for MarkovChainDistribution.

Parameters:

initial (Any | None)
transitions (Mapping[Any, Any] | None)
length (Any | None)

initial: Any | None = None

transitions: Mapping[Any, Any] | None = None

length: Any | None = None

as_dict()[source]

Return initial, transition, and length priors as a plain payload.

Return type:: dict

class MixturePrior(components=(), weights=None)[source]

Bases: object

Component and weight priors for a MixtureDistribution.

Parameters:

components (Sequence[Any])
weights (Any | None)

components: Sequence[Any] = ()

weights: Any | None = None

as_dict()[source]

Return component and weight priors as a plain payload.

Return type:: dict

class NormalGammaPrior(mu0=0.0, kappa=0.0, alpha=1.0, beta=0.0)[source]

Bases: object

Normal-Gamma prior for Gaussian mu and precision tau.

The density is proportional to tau ** (alpha - 1) exp(-beta tau) sqrt(tau) exp(-0.5 * kappa * tau * (mu - mu0) ** 2). Normalizing constants are omitted because MAP fitting only needs objective differences.

Parameters:

mu0 (float)
kappa (float)
alpha (float)
beta (float)

mu0: float = 0.0

kappa: float = 0.0

alpha: float = 1.0

beta: float = 0.0

as_dict()[source]

Return the normalized prior payload consumed by MAP fitters.

Return type:: dict

class OptionalPrior(observed=None, missing=None)[source]

Bases: object

Observed-child and missing-probability priors for OptionalDistribution.

Parameters:

observed (Any | None)
missing (Any | None)

observed: Any | None = None

missing: Any | None = None

as_dict()[source]

Return observed-child and missingness priors as a plain payload.

Return type:: dict

class RecordPrior(fields)[source]

Bases: object

Field priors for a named RecordDistribution.

Parameters:: fields (Mapping[Any, Any])

fields: Mapping[Any, Any]

as_dict()[source]

Return field priors as a plain record-prior payload.

Return type:: dict

as_prior_dict(prior)[source]

Return a plain-Python representation of a prior specification.

Parameters:: prior (Any)
Return type:: Any

beta(alpha, beta, parameter=None)[source]

Create a Beta prior for a unit-interval parameter.

Parameters:

alpha (float)
beta (float)
parameter (str | None)

Return type:

BetaPrior

conditional(conditions, default=None, given=None)[source]

Create a Conditional prior over keyed/default/given child priors.

Parameters:

conditions (Mapping[Any, Any])
default (Any | None)
given (Any | None)

Return type:

ConditionalPrior

composite(children)[source]

Create a Composite prior from child prior specifications.

Parameters:: children (Sequence[Any])
Return type:: CompositePrior

dirichlet(alpha)[source]

Create a Dirichlet prior for simplex-valued parameters.

Parameters:: alpha (Any)
Return type:: DirichletPrior

gamma(shape, rate, parameter=None)[source]

Create a Gamma prior for a positive parameter.

Parameters:

shape (float)
rate (float)
parameter (str | None)

Return type:

GammaPrior

markov_chain(initial=None, transitions=None, length=None)[source]

Create a Markov-chain prior over initial, transition, and length terms.

Parameters:

initial (Any | None)
transitions (Mapping[Any, Any] | None)
length (Any | None)

Return type:

MarkovChainPrior

mixture(components=(), weights=None)[source]

Create a Mixture prior over component and weight priors.

Parameters:

components (Sequence[Any])
weights (Any | None)

Return type:

MixturePrior

normal_gamma(mu0=0.0, kappa=0.0, alpha=1.0, beta=0.0)[source]

Create a Normal-Gamma prior for Gaussian mean/precision parameters.

Parameters:

mu0 (float)
kappa (float)
alpha (float)
beta (float)

Return type:

NormalGammaPrior

optional(observed=None, missing=None)[source]

Create an Optional prior over observed child and missingness terms.

Parameters:

observed (Any | None)
missing (Any | None)

Return type:

OptionalPrior

record(fields)[source]

Create a Record prior from field-name prior specifications.

Parameters:: fields (Mapping[Any, Any])
Return type:: RecordPrior