|---| | [ View source on GitHub](https://github.com/tensorflow/probability/blob/v0.25.0/tensorflow_probability/python/experimental/mcmc/thinning_kernel.py#L28-L116) | Discards samples to perform thinning. Inherits From: [`TransitionKernel`](https://www.tensorflow.org/probability/api_docs/python/tfp/mcmc/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.


| ## Args || |---|---| | `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"). |



| ## Attributes || |---|---| | `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.
`TransitionKernel`s which are "uncalibrated" are often calibrated by composing them with the [`tfp.mcmc.MetropolisHastings`](https://www.tensorflow.org/probability/api_docs/python/tfp/mcmc/MetropolisHastings) `TransitionKernel`. | | `name` |

| | `num_steps_to_skip` |

| | `parameters` |

|
## Methods ### `bootstrap_results` [View source](https://github.com/tensorflow/probability/blob/v0.25.0/tensorflow_probability/python/experimental/mcmc/thinning_kernel.py#L85-L96) bootstrap_results( init_state ) Instantiates a new kernel state with no calls.


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



| Returns || |---|---| | `kernel_results` | `collections.namedtuple` of `Tensor`s representing internal calculations made within this function. |
### `copy` [View source](https://github.com/tensorflow/probability/blob/v0.25.0/tensorflow_probability/python/mcmc/kernel.py#L148-L164) 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` [View source](https://github.com/tensorflow/probability/blob/v0.25.0/tensorflow_probability/python/mcmc/kernel.py#L136-L146) 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` [View source](https://github.com/tensorflow/probability/blob/v0.25.0/tensorflow_probability/python/experimental/mcmc/thinning_kernel.py#L57-L83) one_step( current_state, previous_kernel_results, seed=None ) Collects one non-thinned chain state.


| Args || |---|---| | `current_state` | `Tensor` or Python `list` of `Tensor`s representing the current state(s) of the Markov chain(s), | | `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`](https://www.tensorflow.org/probability/api_docs/python/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. |