swMATH ID: 3488
Software Authors: Marko Laine
Description: This toolbox provides tools to generate and analyse Metropolis-Hastings MCMC chain using multivariate Gaussian proposal distribution. The covariance matrix of the proposal distribution can be adapted during the simulation according to adaptive schemes described in the references. The code can do the following Produce MCMC chain for user written -2*log(likelihood) and -2*log(prior) functions. These will be equal to sum-of-squares functions when using Gaussian likelihood and prior. In case of Gaussian error model, sample the model error variance from the conjugate inverse chi squared distribution. Do plots and statistical analyses based on the chain, such as basic statistics, convergence diagnostics, chain timeseries plots, 2 dimensional clouds of points, kernel densities, and histograms. Calculate densities, cumulative distributions, quantiles, and random variates for some useful common statistical distributions without using Mathworks own statistics toolbox. The code is self consistent, no additional Matlab toolboxes are used. However, a quite recent version of Matlab is needed.
Homepage: http://helios.fmi.fi/~lainema/mcmc/
Programming Languages: None
Operating Systems: None
Dependencies: Matlab
Keywords: adaptive algorithm; Markov chain Monte Carlo; ergodicity; random walk Metropolis algorithms; convergence; numerical tests; Metropolis-Hastings algorithms
Related Software: NUTS; GPstuff; TOMS659; EGO; energy; MCSim; BayesDA; RODAS
Cited in: 9 Publications

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