Skip to main content

Multichain MCMC framework and algorithms

Project description

A simple framework based on PyMC for multichain MCMC algorithms.

Contains working implementations of:
  • DREAM/DREAM_ZS sampler : multichain_mcmc.dream.DreamSampler
  • Adaptive Metropolis Adjusted Langevin Algorithm (AMALA) sampler : multichain_mcmc.amala.AmalaSampler
See the sampler classes for details. AMALA sampler requires PyMC branch with gradient information support to function.
http://github.com/pymc-devs/pymc/tree/gradientBranch

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Filename, size & hash SHA256 hash help File type Python version Upload date
multichain_mcmc-0.3.tar.gz (3.9 MB) Copy SHA256 hash SHA256 Source None

Supported by

Elastic Elastic Search Pingdom Pingdom Monitoring Google Google BigQuery Sentry Sentry Error logging AWS AWS Cloud computing DataDog DataDog Monitoring Fastly Fastly CDN SignalFx SignalFx Supporter DigiCert DigiCert EV certificate StatusPage StatusPage Status page