superstats.transition.deterministic.deterministic_transition#

Base interface and shared helpers for deterministic transitions.

Classes

DeterministicTransition([bounds, initial_prior])

Base class for deterministic transition models.
class superstats.transition.deterministic.deterministic_transition.DeterministicTransition(bounds=None, initial_prior=None)[source]#

Bases: ABC

Base class for deterministic transition models.

Subclasses implement deterministic dynamics for a single scalar latent parameter. Subclasses must implement sample to generate complete trajectories from resolved parameter values.

Parameters:
boundstuple or np.ndarray or None, optional, default: None

Lower and upper bounds for the latent state, applied via scaled_sigmoid. Falls back to DEFAULT_BOUNDS if not provided.

initial_priorPrior or None, optional, default: None

Prior used to draw the initial latent state. Falls back to DEFAULT_INITIAL_PRIOR if not provided.

Attributes:
boundsnp.ndarray

Resolved lower and upper bounds for the latent state.

initial_priorPrior

Resolved prior used to draw initial latent states.

hyper_specsdict

Mapping from parameter names to either a Prior (sampled per batch), a scalar fixed value, or None to use a deterministic default. Subclasses populate this mapping.

transition_namestr

Short model name, such as "linear".

Parameters:
dtype#

alias of float32

abstract sample(batch_size, num_steps)[source]#

Generate batch_size latent trajectories of length num_steps.

Parameters:
batch_sizeint

Number of independent trajectories to draw.

num_stepsint

Number of time steps per trajectory (including initial state).

Returns:
resultdict - dictionary with keys deterministic_params,

hyper_params, and fixed_params. deterministic_params is an ndarray of shape (batch_size, steps).

Parameters:

  • batch_size (int)

  • num_steps (int)

Return type:

Dict[str, Any]

sample_from_parameters(params, batch_size, num_steps)[source]#

Generate trajectories from already-resolved transition parameters.

Subclasses used with posterior predictive resimulation should override this method. params contains the subclass’s own hyperparameter names, independent of any name used by a JointPrior.

Parameters:
Return type:

ndarray