FireandForget MCMC Sampling (ligo.skymap.bayestar.ez_emcee
)¶

ligo.skymap.bayestar.ez_emcee.
ez_emcee
(log_prob_fn, lo, hi, nindep=200, ntemps=10, nwalkers=None, nburnin=500, args=(), kwargs={}, **options)[source] [edit on github]¶ Fireandforget MCMC sampling using
ptemcee.Sampler
, featuring automated convergence monitoring, progress tracking, and thinning.The parameters are bounded in the finite interval described by
lo
andhi
(includingnp.inf
andnp.inf
for halfinfinite or infinite domains).If run in an interactive terminal, live progress is shown including the current sample number, the total required number of samples, time elapsed and estimated time remaining, acceptance fraction, and autocorrelation length.
Sampling terminates when all chains have accumulated the requested number of independent samples.
 Parameters
 log_prob_fncallable
The log probability function. It should take as its argument the parameter vector as an of length
ndim
, or if it is vectorized, a 2D array withndim
columns. lolist,
numpy.ndarray
List of lower limits of parameters, of length
ndim
. hilist,
numpy.ndarray
List of upper limits of parameters, of length
ndim
. nindepint, optional
Minimum number of independent samples.
 ntempsint, optional
Number of temperatures.
 nwalkersint, optional
Number of walkers. The default is 4 times the number of dimensions.
 nburninint, optional
Number of samples to discard during burnin phase.
 Returns
 chain
numpy.ndarray
The thinned and flattened posterior sample chain, with at least
nindep
*nwalkers
rows and exactlyndim
columns.
 chain
 Other Parameters
 kwargs :
Extra keyword arguments for
ptemcee.Sampler
. Tip: Consider setting thepool
orvectorized
keyword arguments in order to speed up likelihood evaluations.
Notes
The autocorrelation length, which has a complexity of \(O(N \log N)\) in the number of samples, is recalculated at geometrically progressing intervals so that its amortized complexity per sample is constant. (In simpler terms, as the chains grow longer and the autocorrelation length takes longer to compute, we update it less frequently so that it is never more expensive than sampling the chain in the first place.)
Examples
>>> from ligo.skymap.bayestar.ez_emcee import ez_emcee >>> from matplotlib import pyplot as plt >>> import numpy as np >>> >>> def log_prob(params): ... """Eggbox function""" ... return 5 * np.log((2 + np.cos(0.5 * params).prod(1))) ... >>> lo = [3*np.pi, 3*np.pi] >>> hi = [+3*np.pi, +3*np.pi] >>> chain = ez_emcee(log_prob, lo, hi, vectorize=True) Sampling: 51%██  8628/16820 [00:04<00:04, 1966.74it/s, accept=0.535, acl=62] >>> plt.plot(chain[:, 0], chain[:, 1], '.')