tfp.experimental.mcmc.sample_chain_with_burnin

Implements Markov chain Monte Carlo via repeated TransitionKernel steps.

tfp.experimental.mcmc.sample_chain_with_burnin(
    num_results,
    current_state,
    previous_kernel_results=None,
    kernel=None,
    num_burnin_steps=0,
    num_steps_between_results=0,
    trace_fn=_trace_current_state,
    parallel_iterations=10,
    seed=None,
    name=None
)

This function samples from a Markov chain at current_state whose stationary distribution is governed by the supplied TransitionKernel instance (kernel).

This function can sample from multiple chains, in parallel. (Whether or not there are multiple chains is dictated by the kernel.)

The current_state can be represented as a single Tensor or a list of Tensors which collectively represent the current state.

Since MCMC states are correlated, it is sometimes desirable to produce additional intermediate states, and then discard them, ending up with a set of states with decreased autocorrelation. See [Owen (2017)][1]. Such 'thinning' is made possible by setting num_steps_between_results > 0. The chain then takes num_steps_between_results extra steps between the steps that make it into the results. The extra steps are never materialized, and thus do not increase memory requirements.

In addition to returning the chain state, this function supports tracing of auxiliary variables used by the kernel. The traced values are selected by specifying trace_fn. By default, all chain states but no kernel results are traced.

Args
`num_results`	Integer number of Markov chain draws.
`current_state`	`Tensor` or Python `list` of `Tensor`s representing the current state(s) of the Markov chain(s).
`previous_kernel_results`	A `Tensor` or a nested collection of `Tensor`s representing internal calculations made within the previous call to this function (or as returned by `bootstrap_results`).
`kernel`	An instance of `tfp.mcmc.TransitionKernel` which implements one step of the Markov chain.
`num_burnin_steps`	Integer number of chain steps to take before starting to collect results. Default value: 0 (i.e., no burn-in).
`num_steps_between_results`	Integer number of chain steps between collecting a result. Only one out of every `num_steps_between_samples + 1` steps is included in the returned results. The number of returned chain states is still equal to `num_results`. Default value: 0 (i.e., no thinning).
`trace_fn`	A callable that takes in the current chain state and the previous kernel results and return a `Tensor` or a nested collection of `Tensor`s that is then traced along with the chain state.
`parallel_iterations`	The number of iterations allowed to run in parallel. It must be a positive integer. See `tf.while_loop` for more details.
`seed`	PRNG seed; see `tfp.random.sanitize_seed` for details.
`name`	Python `str` name prefixed to Ops created by this function. Default value: `None` (i.e., 'experimental_mcmc_sample_chain_with_burnin').

Returns
`result`	A `RunKernelResults` instance containing information about the sampling run. Main field is `trace`, the history of outputs of `trace_fn`. See `RunKernelResults` for contents of other fields.

References

[1]: Art B. Owen. Statistically efficient thinning of a Markov chain sampler. Technical Report, 2017. http://statweb.stanford.edu/~owen/reports/bestthinning.pdf

tfp.experimental.mcmc.sample_chain_with_burnin

Args

Returns

References