Metropolis-Hastings Sampler 1.0

Date Added: May 10, 2013 | Visits: 487

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The Metropolis-Hastings Sampler is the most common Markov-Chain-Monte-Carlo (MCMC) algorithm used to sample from arbitrary probability density functions (PDF). Suppose you want to simulate samples from a random variable which can be described by an arbitrary PDF, i.e., any function which integrates to 1 over a given interval. This algorithm will do just that, as illustrated by the Plot done with Matplotlib. Notice how the samples follow the theoretical PDF.

Requirements: No special requirements

Platforms: Windows, Mac, *nix, Mac OSX, Linux, Python , BSD Solaris

Keyword: Hastings, Method, Metropolis, Metropolis Hastings Method, Random Numbers, Sampling Probability Density

Users rating: 0/10

License: Freeware

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