在给定收敛标准的情况下，在司仪中实施提前停止

Question

我正在使用emcee.EnsembleSampler来采样一些可能性。我在数百万个不同的数据集上执行此操作，因此性能很重要。

一旦链满足自相关时间的某个收敛标准，能够让链停止采样，那就太好了。

我在文档中或通过我的搜索找不到任何方法，所以希望有人有它的配方。

Answer 1

虽然我也在当前的 emcee 文档中找不到任何东西，但最新版本似乎添加了一个教程，它恰好显示了您正在尝试做的事情：链接

如果链接断开或情况发生变化，下面是以下示例：

import emcee
import numpy as np
np.random.seed(42)
# The definition of the log probability function
# We'll also use the "blobs" feature to track the "log prior" for each step
def log_prob(theta):
log_prior = -0.5 *  np.sum((theta-1.0)**2 / 100.0)
log_prob = -0.5 * np.sum(theta**2) + log_prior
return log_prob, log_prior
# Initialize the walkers
coords = np.random.randn(32, 5)
nwalkers, ndim = coords.shape
# Set up the backend
# Don't forget to clear it in case the file already exists
filename = "tutorial.h5"
backend = emcee.backends.HDFBackend(filename)
backend.reset(nwalkers, ndim)
# Initialize the sampler
sampler = emcee.EnsembleSampler(nwalkers, ndim, log_prob, backend=backend)
max_n = 100000
# We'll track how the average autocorrelation time estimate changes
index = 0
autocorr = np.empty(max_n)
# This will be useful to testing convergence
old_tau = np.inf
# Now we'll sample for up to max_n steps
for sample in sampler.sample(coords, iterations=max_n, progress=True):
# Only check convergence every 100 steps
if sampler.iteration % 100:
continue
# Compute the autocorrelation time so far
# Using tol=0 means that we'll always get an estimate even
# if it isn't trustworthy
tau = sampler.get_autocorr_time(tol=0)
autocorr[index] = np.mean(tau)
index += 1
# Check convergence
converged = np.all(tau * 100 < sampler.iteration)
converged &= np.all(np.abs(old_tau - tau) / tau < 0.01)
if converged:
break
old_tau = tau