I'm trying to sample multiple chains in PyMC3. In PyMC2 I would do something like this:
for i in range(N):
model.sample(iter=iter, burn=burn, thin = thin)
How should I do the same thing in PyMC3? I saw there is a 'njobs' argument in the 'sample' method, but it throws an error when I set a value for it. I want to use sampled chains to get 'pymc.gelman_rubin' output.
A chain is a single run of MCMC. So if you have six 2-d parameters in your model and ask for 1000 samples, you will get six 2x1000 arrays for each chain. When running MCMC, it is a best practice to use multiple chains, as they can help diagnose problems.
A design goal of PyMC3 is to let the user worry about statistical modelling, and not worry about inference, and tuning attempts to automatically set some of the dozens of knobs available in modern MCMC methods.
PyMC3 is a probabilistic programming package for Python that allows users to fit Bayesian models using a variety of numerical methods, most notably Markov chain Monte Carlo (MCMC) and variational inference (VI). Its flexibility and extensibility make it applicable to a large suite of problems.
PyMC3 is the current version of the PyMC open source probabilistic programming framework for Python, having been forked over 1,300 times and starred over 5,400 times on GitHub.
Better is to use njobs
to run chains in parallel:
#!/usr/bin/env python3
import pymc3 as pm
import numpy as np
from pymc3.backends.base import merge_traces
xobs = 4 + np.random.randn(20)
model = pm.Model()
with model:
mu = pm.Normal('mu', mu=0, sd=20)
x = pm.Normal('x', mu=mu, sd=1., observed=xobs)
step = pm.NUTS()
with model:
trace = pm.sample(1000, step, njobs=2)
If you love us? You can donate to us via Paypal or buy me a coffee so we can maintain and grow! Thank you!
Donate Us With