For more details on UncalibratedRandomWalk, see
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.
Python callable which takes a list of state parts and a
seed; returns a same-type list of Tensors, 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
A structure of string names indicating how
members of the state are sharded.
Python str name prefixed to Ops created by this function.
Default value: None (i.e., 'rwm_kernel').
if there isn't one scale or a list with same length as
The shard axis names for members of the state.
Returns True if Markov chain converges to specified distribution.
TransitionKernels which are "uncalibrated" are often calibrated by
composing them with the tfp.mcmc.MetropolisHastingsTransitionKernel.
Return dict of __init__ arguments and their values.
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.,