import abc
from probnum.filtsmooth.statespace.transition import Transition
__all__ = ["DiscreteModel"]
class DiscreteModel(Transition):
"""
Transition models for discretely indexed processes.
Transformations of the form
.. math:: x_{t + \\Delta t} \\sim p(x_{t + \\Delta t} | x_t) .
As such, compatible with Bayesian filtering and smoothing algorithms.
See Also
--------
:class:`ContinuousModel`
Transition models for continuously indexed processes.
:class:`BayesFiltSmooth`
Bayesian filtering and smoothing algorithms.
"""
[docs] @abc.abstractmethod
def transition_realization(self, real, start, stop, **kwargs):
raise NotImplementedError
[docs] @abc.abstractmethod
def transition_rv(self, rv, start, stop, **kwargs):
raise NotImplementedError
@property
@abc.abstractmethod
def dimension(self):
raise NotImplementedError