t-walk on the banana side

March 14, 2010
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(This article was first published on Xi'an's Og » R, and kindly contributed to R-bloggers)

Following my remarks on the t-walk algorithm in the recent A General Purpose Sampling Algorithm for Continuous Distributions, published by Christen and Fox in Bayesian Analysis that acts like a general purpose MCMC algorithm, Darren Wraith tested it on the generic (10 dimension) banana target we used in the cosmology paper. Here is an output from his comparison R program:

The use of the t-walk algorithm (left, with the same number of particles) in this very special case thus produces a wider variability  on the estimated means than the use of adaptive MCMC (center) and our (tuned) PMC algorithm (right).


Filed under: R, Statistics Tagged: adaptive MCMC methods, MCMC, Monte Carlo methods, population Monte Carlo, random walk

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