multichain_mcmc 0.2.2

Multichain MCMC framework and algorithms

A simple framework based on PyMC for multichain MCMC algorithms.

Contains working implementations of:

1. DREAM/DREAM_ZS sampler

2. Adaptive Metropolis Adjusted Langevin Algorithm (AMALA) sampler

1. DREAM_ZSimplementation based on the algorithms presented in the following two papers:

   C.J.F. ter Braak, and J.A. Vrugt, Differential evolution Markov chain with snooker updater and fewer chains, Statistics and Computing, 18(4), 435-446, doi:10.1007/s11222-008-9104-9, 2008.

   J.A. Vrugt, C.J.F. ter Braak, C.G.H. Diks, D. Higdon, B.A. Robinson, and J.M. Hyman, Accelerating Markov chain Monte Carlo simulation by differential evolution with self-adaptive randomized subspace sampling, International Journal of Nonlinear Sciences and Numerical Simulation, 10(3), 273-290, 2009.

2. AMALA implementation based on

   AMALA sampler requires PyMC branch with gradient information support to function. http://github.com/pymc-devs/pymc/tree/gradientBranch