tfp.experimental.mcmc.ThinningKernel

Discards samples to perform thinning.

Inherits From: TransitionKernel

tfp.experimental.mcmc.ThinningKernel(
    inner_kernel, num_steps_to_skip, name=None
)

ThinningKernel is a composable TransitionKernel that thins samples returned by its inner_kernel. All Transition Kernels wrapping it will only see non-discarded samples.

inner_kernel TransitionKernel whose one_step will generate MCMC results.
num_steps_to_skip Integer or scalar Tensor representing the number of chain steps skipped before collecting a result.
name Python str name prefixed to Ops created by this function. Default value: None (i.e., "thinning_kernel").

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

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

num_steps_to_skip

parameters

Methods

bootstrap_results

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bootstrap_results(
    init_state
)

Instantiates a new kernel state with no calls.

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

Returns
kernel_results collections.namedtuple 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).

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
)

Collects one non-thinned chain state.

Args
current_state Tensor or Python list of Tensors representing the current state(s) of the Markov chain(s),
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
new_chain_state Newest non-discarded MCMC chain state drawn from the inner_kernel.
kernel_results collections.namedtuple of internal calculations used to advance the chain.