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multichain_mcmc 0.2.2

Multichain MCMC framework and algorithms

Latest Version: 0.3

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

 
File Type Py Version Uploaded on Size
multichain_mcmc-0.2.2.tar.gz (md5) Source 2010-05-15 2MB