tfp.mcmc.UncalibratedRandomWalk

Generate proposal for the Random Walk Metropolis algorithm.

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

For more details on UncalibratedRandomWalk, see RandomWalkMetropolis.

Args
`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 `Tensor`s, 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').

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

Attributes
`experimental_shard_axis_names`	The shard axis names for members of the state.
`is_calibrated`	Returns `True` if Markov chain converges to specified distribution. `TransitionKernel`s 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(
    init_state
)

Creates initial previous_kernel_results using a supplied state.

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(
    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(
    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 `Tensor`s 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 `Tensor`s 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 `Tensor`s 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`.