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Runs one step of Metropolis-adjusted Langevin algorithm.

Inherits From: TransitionKernel

Metropolis-adjusted Langevin algorithm (MALA) is a Markov chain Monte Carlo (MCMC) algorithm that takes a step of a discretised Langevin diffusion as a proposal. This class implements one step of MALA using Euler-Maruyama method for a given current_state and diagonal preconditioning volatility matrix. Mathematical details and derivations can be found in [Roberts and Rosenthal (1998)] and [Xifara et al. (2013)].

See UncalibratedLangevin class description below for details on the proposal generating step of the algorithm.

The one_step function can update multiple chains in parallel. It assumes that all leftmost dimensions of current_state index independent chain states (and are therefore updated independently). The output of target_log_prob_fn(*current_state) should reduce log-probabilities across all event dimensions. Slices along the rightmost dimensions may have different target distributions; for example, current_state[0, :] could have a different target distribution from current_state[1, :]. These semantics are governed by target_log_prob_fn(*current_state). (The number of independent chains is tf.size(target_log_prob_fn(*current_state)).)

Examples:

Simple chain with warm-up.

In this example we sample from a standard univariate normal distribution using MALA with step_size equal to 0.75.

import tensorflow as tf
import tensorflow_probability as tfp
import numpy as np

tfd = tfp.distributions

dtype = np.float32

with tf.Session(graph=tf.Graph()) as sess:
# Target distribution is Standard Univariate Normal
target = tfd.Normal(loc=dtype(0), scale=dtype(1))

# Define MALA sampler with `step_size` equal to 0.75
samples, _ = tfp.mcmc.sample_chain(
num_results=1000,
current_state=dtype(1),
target_log_prob_fn=target.log_prob,
step_size=0.75,
seed=42),
num_burnin_steps=500,
parallel_iterations=1)  # For determinism.

sample_mean = tf.reduce_mean(samples, axis=0)
sample_std = tf.sqrt(
tf.reduce_mean(tf.squared_difference(samples, sample_mean),
axis=0))

sess.graph.finalize()  # No more graph building.

[sample_mean_, sample_std_] = sess.run([sample_mean, sample_std])

print('sample mean', sample_mean_)
print('sample standard deviation', sample_std_)
Same example but in eager mode.
import tensorflow as tf
import tensorflow_probability as tfp
import numpy as np
import matplotlib.pyplot as plt

# Support for eager execution
tf.enable_eager_execution()

tfd = tfp.distributions
dtype = np.float32

# Target distribution is Standard Univariate Normal
target = tfd.Normal(loc=dtype(0), scale=dtype(1))

def target_log_prob(x):
return target.log_prob(x)

# Define MALA sampler with `step_size` equal to 0.75
samples, _ = tfp.mcmc.sample_chain(
num_results=1000,
current_state=dtype(1),
target_log_prob_fn=target_log_prob,
step_size=0.75,
seed=42),
num_burnin_steps=500,
parallel_iterations=1)  # For determinism.

sample_mean = tf.reduce_mean(samples, axis=0)
sample_std = tf.sqrt(
tf.reduce_mean(tf.squared_difference(samples, sample_mean),
axis=0))

print('sample mean', sample_mean)
print('sample standard deviation', sample_std)

plt.title('Traceplot')
plt.plot(samples.numpy(), 'b')
plt.xlabel('Iteration')
plt.ylabel('Position')
plt.show()
Sample from a 3-D Multivariate Normal distribution.

In this example we also consider a non-constant volatility function.

import tensorflow as tf
import tensorflow_probability as tfp
import numpy as np

tfd = tfp.distributions

dtype = np.float32
true_mean = dtype([0, 0, 0])
true_cov = dtype([[1, 0.25, 0.25], [0.25, 1, 0.25], [0.25, 0.25, 1]])
num_results = 500
num_chains = 500

with tf.Session(graph=tf.Graph()) as sess:
# Target distribution is defined through the Cholesky decomposition
chol = tf.linalg.cholesky(true_cov)
target = tfd.MultivariateNormalTriL(loc=true_mean, scale_tril=chol)

# Assume that the state is passed as a list of tensors `x` and `y`.
# Then the target log-density is defined as follows:
def target_log_prob(x, y):
# Stack the input tensors together
z = tf.concat([x, y], axis=-1) - true_mean
return target.log_prob(z)

# Here we define the volatility function to be non-constant
def volatility_fn(x, y):
# Stack the input tensors together
return [1. / (0.5 + 0.1 * tf.sqrt(x * x)),
1. / (0.5 + 0.1 *tf.sqrt(y * y))]

# Initial state of the chain
init_state = [np.ones([num_chains, 2], dtype=dtype),
np.ones([num_chains, 1], dtype=dtype)]

# Run MALA with normal proposal for `num_results` iterations for
# `num_chains` independent chains:
states, _ = tfp.mcmc.sample_chain(
num_results=num_results,
current_state=init_state,
target_log_prob_fn=target_log_prob,
step_size=.1,
volatility_fn=volatility_fn,
seed=42),
num_burnin_steps=200,
num_steps_between_results=1,
parallel_iterations=1)

states = tf.concat(states, axis=-1)
sample_mean = tf.reduce_mean(states, axis=[0, 1])
x = tf.expand_dims(states - sample_mean, -1)
sample_cov = tf.reduce_mean(
tf.matmul(x, tf.transpose(x, [0, 1, 3, 2])), [0, 1])

[sample_mean_, sample_cov_] = sess.run([
sample_mean, sample_cov])

print('sample mean', sample_mean_)
print('sample covariance matrix', sample_cov_)

: Gareth Roberts and Jeffrey Rosenthal. Optimal Scaling of Discrete Approximations to Langevin Diffusions. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 60: 255-268, 1998. https://doi.org/10.1111/1467-9868.00123

: T. Xifara et al. Langevin diffusions and the Metropolis-adjusted Langevin algorithm. arXiv preprint arXiv:1309.2983, 2013. https://arxiv.org/abs/1309.2983

__init__

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__init__(
target_log_prob_fn,
step_size,
volatility_fn=None,
seed=None,
parallel_iterations=10,
name=None
)

Initializes MALA transition kernel.

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.
• 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.
• volatility_fn: Python callable which takes an argument like current_state (or *current_state if it's a list) and returns volatility value at current_state. Should return a Tensor or Python list of Tensors that must broadcast with the shape of current_state Defaults to the identity function.
• seed: Python integer to seed the random number generator.
• parallel_iterations: the number of coordinates for which the gradients of the volatility matrix volatility_fn can be computed in parallel. Default value: None (i.e., no seed).
• name: Python str name prefixed to Ops created by this function. Default value: None (i.e., 'mala_kernel').

Returns:

• next_state: Tensor or Python list of Tensors representing the state(s) of the Markov chain(s) at each result step. Has same 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.
• TypeError: if volatility_fn is not callable.

Properties

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.

parameters

Return dict of __init__ arguments and their values.

Methods

bootstrap_results

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bootstrap_results(init_state)

Creates initial previous_kernel_results using a supplied state.

one_step

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one_step(
current_state,
previous_kernel_results
)

Runs one iteration of MALA.

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 step_size or a list with same length as current_state or diffusion_drift.