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
Python integer to seed the random number generator.
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
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.