tfp.substrates.numpy.mcmc.UncalibratedRandomWalk

Generate proposal for the Random Walk Metropolis algorithm.

Inherits From: TransitionKernel

Main aliases

tfp.experimental.substrates.numpy.mcmc.UncalibratedRandomWalk

 
tfp.substrates.numpy.mcmc.UncalibratedRandomWalk(
    target_log_prob_fn,
    new_state_fn=None,
    experimental_shard_axis_names=None,
    name=None
)

For more details on UncalibratedRandomWalk, see RandomWalkMetropolis.

target_log_prob_fn Python callable which takes an argument like current_state (or *current_state if it's a list) and returns its (possibly unnormalized) log-density under the target distribution.
new_state_fn Python callable which takes a list of state parts and a seed; returns a same-type list of Tensors, each being a perturbation of the input state parts. The perturbation distribution is assumed to be a symmetric distribution centered at the input state part. Default value: None which is mapped to tfp.mcmc.random_walk_normal_fn().
experimental_shard_axis_names A structure of string names indicating how members of the state are sharded.
name Python str name prefixed to Ops created by this function. Default value: None (i.e., 'rwm_kernel').

ValueError if there isn't one scale or a list with same length as current_state.

experimental_shard_axis_names The shard axis names for members of the state.
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.

name

new_state_fn

parameters Return dict of __init__ arguments and their values.
target_log_prob_fn

Methods

bootstrap_results

View source

bootstrap_results(
    init_state
)

Creates initial previous_kernel_results using a supplied state.

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).

experimental_with_shard_axes

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experimental_with_shard_axes(
    shard_axis_names
)

Returns a copy of the kernel with the provided shard axis names.

Args
shard_axis_names a structure of strings indicating the shard axis names for each component of this kernel's state.

Returns
A copy of the current kernel with the shard axis information.

one_step

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one_step(
    current_state, previous_kernel_results, seed=None
)

Runs one iteration of Random Walk Metropolis with normal proposal.

Args
current_state Tensor or Python list of Tensors representing the current state(s) of the Markov chain(s). The first r dimensions index independent chains, r = tf.rank(target_log_prob_fn(*current_state)).
previous_kernel_results collections.namedtuple containing Tensors representing values from previous calls to this function (or from the bootstrap_results function.)
seed PRNG seed; see tfp.random.sanitize_seed for details.

Returns
next_state Tensor or Python list of Tensors representing the state(s) of the Markov chain(s) after taking exactly one step. Has same type and shape as current_state.
kernel_results collections.namedtuple of internal calculations used to advance the chain.

Raises
ValueError if there isn't one scale or a list with same length as current_state.