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Weak convergence and optimal scaling of random walk Metropolis algorithms
This paper considers the problem of scaling the proposal distribution of a multidimensional random walk Metropolis algorithm in order to maximize the efficiency of the algorithm. The main result is aExpand
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Adaptive Rejection Sampling for Gibbs Sampling
SUMMARY We propose a method for rejection sampling from any univariate log-concave probability density function. The method is adaptive: as sampling proceeds, the rejection envelope and the squeezingExpand
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Introducing Markov chain Monte Carlo
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Derivative-free adaptive rejection sampling for Gibbs sampling
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Markov Chain Monte Carlo
Markov chain Monte Carlo (MCMC) is a technique for estimating by simulation the expectation of a statistic in a complex model. Successive random selections form a Markov chain, the stationaryExpand
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Fitting Bayesian multiple random effects models
TLDR
We present an analysis of data on immunity after Rubella vaccinations which results in a slow-mixing Gibbs sampler. Expand
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Markov Chain Monte Carlo in practica
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Permutation Tests for Analysing Cospeciation in Multiple Phylogenies
The purpose of this study is to develop permutation test statistics that can be used to analyse cospeciation in three phylogenies. The null hypothesis, H0, is that the three phylogenies are notExpand
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