pymc-submodels: Common Bayesian submodels for PyMC

Author:	Daniel E. Acuna <acuna@principiapredicitva.com> Copyright (c) 2010-2011
URL:	http://blog.principiapredictiva.com

Description

Common submodels used in Bayesian modeling, implemented in PyMC.

Contents

1. Truncated GEM distribution (sticks) for Dirichlet Process
2. Gaussian finite and infinite mixtures (based on truncated GEM distribution)

Example

Infinite mixture:

import numpy as np
import pymc as pm
from pymc_submodels import infty_mixture_model as imm

data = np.array([10., 11., 12., -10., -11., -12.])
# Precision of Gaussian likelihood
tau_like = 0.5
# Mean and precision of Gaussian base distribution
mu_base = 0.
tau_base = 0.01

# Concentration parameter of Dirichlet process
alpha = 1.

# Construct model using pymc_submodels package
mdl = imm.model(data, tau_like, mu_base, tau_base, alpha)

# Call PyMC
mcmc = pm.MCMC(mdl)
# Sample
mcmc.sample(10000, 1000, 2)
# Stochastic 'z' component membership of each data point
print np.mean(mcmc.trace('z')[:], 0)