For more details on UncalibratedRandomWalk, see
RandomWalkMetropolis.
Args
target_log_prob_fn
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.
new_state_fn
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
tfp.mcmc.random_walk_normal_fn().
seed
Python integer to seed the random number generator.
name
Python str name prefixed to Ops created by this function.
Default value: None (i.e., 'rwm_kernel').
Raises
ValueError
if there isn't one scale or a list with same length as
current_state.
Attributes
is_calibrated
Returns True if Markov chain converges to specified distribution.
TransitionKernels which are "uncalibrated" are often calibrated by
composing them with the tfp.mcmc.MetropolisHastingsTransitionKernel.
name
new_state_fn
parameters
Return dict of __init__ arguments and their values.
Runs one iteration of Random Walk Metropolis with normal proposal.
Args
current_state
Tensor or Python list of Tensors representing the
current state(s) of the Markov chain(s). The first r dimensions index
independent chains, r = tf.rank(target_log_prob_fn(*current_state)).
previous_kernel_results
collections.namedtuple containing Tensors
representing values from previous calls to this function (or from the
bootstrap_results function.)
Returns
next_state
Tensor or Python list of Tensors representing the state(s)
of the Markov chain(s) after taking exactly one step. Has same type and
shape as current_state.
kernel_results
collections.namedtuple of internal calculations used to
advance the chain.
Raises
ValueError
if there isn't one scale or a list with same length as
current_state.
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