tfp.experimental.substrates.jax.mcmc.UncalibratedHamiltonianMonteCarlo

Runs one step of Uncalibrated Hamiltonian Monte Carlo.

Inherits From: TransitionKernel

For more details on UncalibratedHamiltonianMonteCarlo, see HamiltonianMonteCarlo.

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.
step_size Tensor or Python list of Tensors representing the step size for the leapfrog integrator. Must broadcast with the shape of current_state. Larger step sizes lead to faster progress, but too-large step sizes make rejection exponentially more likely. When possible, it's often helpful to match per-variable step sizes to the standard deviations of the target distribution in each variable.
num_leapfrog_steps Integer number of steps to run the leapfrog integrator for. Total progress per HMC step is roughly proportional to step_size * num_leapfrog_steps.
state_gradients_are_stopped Python bool indicating that the proposed new state be run through tf.stop_gradient. This is particularly useful when combining optimization over samples from the HMC chain. Default value: False (i.e., do not apply stop_gradient).
seed Python integer to seed the random number generator. Deprecated, pass seed to tfp.mcmc.sample_chain.
store_parameters_in_results If True, then step_size and num_leapfrog_steps are written to and read from eponymous fields in the kernel results objects returned from one_step and bootstrap_results. This allows wrapper kernels to adjust those parameters on the fly.
name Python str name prefixed to Ops created by this function. Default value: None (i.e., 'hmc_kernel').

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.MetropolisHastings TransitionKernel.

name

num_leapfrog_steps Returns the num_leapfrog_steps parameter.

If store_parameters_in_results argument to the initializer was set to True, this only returns the value of the num_leapfrog_steps placed in the kernel results by the bootstrap_results method. The actual num_leapfrog_steps in that situation is governed by the previous_kernel_results argument to one_step method.

parameters Return dict of __init__ arguments and their values.
seed

state_gradients_are_stopped

step_size Returns the step_size parameter.

If store_parameters_in_results argument to the initializer was set to True, this only returns the value of the step_size placed in the kernel results by the bootstrap_results method. The actual step size in that situation is governed by the previous_kernel_results argument to one_step method.

target_log_prob_fn

Methods

bootstrap_results

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Creates initial previous_kernel_results using a supplied state.

copy

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Non-destructively creates a deep copy of the kernel.

Args
**override_parameter_kwargs Python String/value dictionary of initialization arguments to override with new values.

Returns
new_kernel 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., dict(self.parameters, **override_parameters_kwargs).

one_step

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Runs one iteration of Hamiltonian Monte Carlo.

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.)
seed Optional, a seed for reproducible sampling.

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 step_size or a list with same length as current_state.