Markov Chain Monte Carlo sampling of posterior distribution

version (4.29 KB) by Aslak Grinsted
MCMC sampling of using a cascaded metropolis


Updated 4 May 2015

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NOTE: I recommend using my new GWMCMC sampler which can also be downloaded from the file exchange:
Markov Chain Monte Carlo sampling of posterior distribution

A metropolis sampler
initialm: starting point fopr random walk
loglikelihood: function handle to likelihood function: logL(m)
logprior: function handle to the log model priori probability: logPapriori(m)
stepfunction: function handle with no inputs which returns a random
step in the random walk. (note stepfunction can also be a
matrix describing the size of a normally distributed
mccount: How long should the markov chain be?
skip: Thin the chain by only storing every N'th step [default=10]

EXAMPLE USAGE: fit a normal distribution to data
logmodelprior=@(m)0; %use a flat prior.
minit=[0 1];
m=mcmc(minit,loglike,logmodelprior,[.2 .5],10000);
m(1:100,:)=[]; %crop drift

--- Aslak Grinsted 2010

Cite As

Aslak Grinsted (2022). Markov Chain Monte Carlo sampling of posterior distribution (, MATLAB Central File Exchange. Retrieved .

MATLAB Release Compatibility
Created with R2010a
Compatible with any release
Platform Compatibility
Windows macOS Linux

Inspired by: Ensemble MCMC sampler

Inspired: Ensemble MCMC sampler

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