tfp.mcmc.TransitionKernel

See Stable See Nightly

Base class for all MCMC TransitionKernels.

This class defines the minimal requirements to efficiently implement a Markov chain Monte Carlo (MCMC) transition kernel. A transition kernel returns a new state given some old state. It also takes (and returns) "side information" which may be used for debugging or optimization purposes (i.e, to "recycle" previously computed results).

Attributes
is_calibrated	Returns True if Markov chain converges to specified distribution. TransitionKernels which are "uncalibrated" are often calibrated by composing them with the tfp.mcmc.MetropolisHastings TransitionKernel.

Methods

bootstrap_results

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bootstrap_results(
    init_state
)

Returns an object with the same type as returned by one_step(...)[1].

Args
init_state	Tensor or Python list of Tensors representing the initial state(s) of the Markov chain(s).

Returns
kernel_results	A (possibly nested) tuple, namedtuple or list of Tensors representing internal calculations made within this function.

copy

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copy(
    **override_parameter_kwargs
)

Non-destructively creates a deep copy of the kernel.

Args
**override_parameter_kwargs	Python String/value dictionary of initialization arguments to override with new values.

Returns
new_kernel	TransitionKernel object of same type as self, initialized with the union of self.parameters and override_parameter_kwargs, with any shared keys overridden by the value of override_parameter_kwargs, i.e., dict(self.parameters, **override_parameters_kwargs).

one_step

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@abc.abstractmethod
one_step(
    current_state, previous_kernel_results, seed=None
)

Takes one step of the TransitionKernel.

Must be overridden by subclasses.

Args
current_state	Tensor or Python list of Tensors representing the current state(s) of the Markov chain(s).
previous_kernel_results	A (possibly nested) tuple, namedtuple or list of Tensors representing internal calculations made within the previous call to this function (or as returned by bootstrap_results).
seed	Optional, a seed for reproducible sampling.

Returns
next_state	Tensor or Python list of Tensors representing the next state(s) of the Markov chain(s).
kernel_results	A (possibly nested) tuple, namedtuple or list of Tensors representing internal calculations made within this function.