superstats.transition.stochastic.stochastic_transition#

Base stochastic transition interface.

Classes

StochasticTransition([bounds, initial_prior])

Base class for stochastic transition models.

class superstats.transition.stochastic.stochastic_transition.StochasticTransition(bounds=None, initial_prior=None)[source]#

Bases: ABC

Base class for stochastic 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 initial latent states. 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 hyperparameter names to either a Prior (to be sampled per-batch) or a scalar fixed value. Populated by subclasses.
transition_namestr: Short model name, such as "rw" or "ar1".

Parameters:

bounds (Sequence[float, float] | None)
initial_prior (Prior | None)

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 at least keys local_params: (ndarray of shape (batch_size, steps)), hyper_params, and fixed_params describing sampled and fixed hyperparameters

Parameters:

batch_size (int)
num_steps (int)

Return type:

Dict[str, Any]