SampleDiscardingKernel is a composable TransitionKernel that
applies thinning and burn-in to samples returned by its
inner_kernel. All Transition Kernels wrapping it will only
see non-discarded samples.
The burn-in step conducts both burn-in and one step of thinning.
In other words, the first call to one_step will skip
num_burnin_steps + num_steps_between_results samples. All
subsequent calls skip only num_steps_between_results samples.
TransitionKernel whose one_step will generate
Integer or scalar Tensor representing the number
of chain steps to take before starting to collect results.
Defaults to 0 (i.e., no burn-in).
Integer or scalar Tensor representing
the 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. Defaults to 0 (i.e., no thinning).
Python str name prefixed to Ops created by this function.
Default value: None (i.e., "sample_discarding_kernel").
Returns True if Markov chain converges to specified distribution.
TransitionKernels which are "uncalibrated" are often calibrated by
composing them with the tfp.mcmc.MetropolisHastingsTransitionKernel.
Non-destructively creates a deep copy of the kernel.
Python String/value dictionary of
initialization arguments to override with new values.
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.,