使用Tensorflow Probability的RandomWalkMetropolis函数执行RandomWalk步骤

问题描述

我是Tensorflow Probability的新手，并且想进行RandomWalk Montecarlo仿真.假设我有张量r代表状态.我希望tfp.mcmc.RandomWalkMetropolis函数返回新状态r'的提议.

I am new to Tensorflow Probability and would like to do a RandomWalk Montecarlo simulation. Let's say I have tensor r that represents a state. I want the tfp.mcmc.RandomWalkMetropolis function to return a proposal for a new state r'.

tfp.mcmc.RandomWalkMetropolis(r)
>>> <tensorflow_probability.python.mcmc.random_walk_metropolis.RandomWalkMetropolis object at 0x14abed2185c0>

仅返回此RandomWalkMetropolis对象，而不是相同状态或微扰状态. RandomWalkMetropolis类还包含函数one_step，但它需要"previous_kernel_results"(我没有)，因为我希望这是我的第一步.另外，如何进一步指定"Metropolis接受/拒绝"步骤?

Instead of the same state, or a slightly perturbed state only this RandomWalkMetropolis object is returned. The RandomWalkMetropolis class also contains the function one_step, but it requires 'previous_kernel_results', which I don't have, because I want this to be my first step. Also, how do I specify the Metropolis accept/reject step further?

推荐答案

RWM是python对象，可通过bootstrap_results和one_step方法使用.例如:

RWM is a python object, which is used via the bootstrap_results and one_step methods. For example:

# TF/TFP Imports
!pip install --quiet tfp-nightly tf-nightly
import tensorflow.compat.v2 as tf
tf.enable_v2_behavior()
import tensorflow_probability as tfp
tfd = tfp.distributions
tfb = tfp.bijectors
import matplotlib.pyplot as plt


def log_prob(x):
  return tfd.Normal(0, 1).log_prob(x)

kernel = tfp.mcmc.RandomWalkMetropolis(log_prob)
state = tfd.Normal(0, 1).sample()
extra = kernel.bootstrap_results(state)
samples = []
for _ in range(1000):
  state, extra = kernel.one_step(state, extra)
  samples.append(state)

plt.hist(samples, bins=20)

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