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Adaptive Direction Sampling
W. R. Gilks, G. O. Roberts and E. I. George
Journal of the Royal Statistical Society. Series D (The Statistician)
Vol. 43, No. 1, Special Issue: Conference on Practical Bayesian Statistics, 1992 (3) (1994), pp. 179-189
Stable URL: http://www.jstor.org/stable/2348942
Page Count: 11
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Markov chain Monte Carlo (MCMC) techniques, such as the Gibbs sampler, are increasingly being used for Bayesian inference. We propose a new MCMC method: adaptive direction sampling (ADS) which, unlike the Gibbs sampler, involves sampling in directions which adapt to the target density. We present non-technically the essence of ADS, but with sufficient detail to allow the practitioner to apply the method. We demonstrate irreducibility of the snooker algorithm, a special case of ADS. We compare the performance of special cases of ADS, including the snooker algorithm and the Gibbs sampler, in a simple test example.
Journal of the Royal Statistical Society. Series D (The Statistician) © 1994 Royal Statistical Society