Results 241 to 250 of about 167,396 (260)
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Statistics and Computing, 2006
We propose to combine two quite powerful ideas that have recently appeared in the Markov chain Monte Carlo literature: adaptive Metropolis samplers and delayed rejection. The ergodicity of the resulting non-Markovian sampler is proved, and the efficiency of the combination is demonstrated with various examples.
H. HAARIO +3 more
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We propose to combine two quite powerful ideas that have recently appeared in the Markov chain Monte Carlo literature: adaptive Metropolis samplers and delayed rejection. The ergodicity of the resulting non-Markovian sampler is proved, and the efficiency of the combination is demonstrated with various examples.
H. HAARIO +3 more
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2019
Abstract The purpose of this chapter is to illustrate some of the things that can go wrong in Markov Chain Monte Carlo (MCMC) analysis and to introduce some diagnostic tools that help identify whether the results of such an analysis can be trusted.
Therese M. Donovan, Ruth M. Mickey
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Abstract The purpose of this chapter is to illustrate some of the things that can go wrong in Markov Chain Monte Carlo (MCMC) analysis and to introduce some diagnostic tools that help identify whether the results of such an analysis can be trusted.
Therese M. Donovan, Ruth M. Mickey
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2014
AbstractThis chapter provides a detailed introduction to modern Bayesian computation. The Metropolis–Hastings algorithm is illustrated using a simple example of distance estimation between two sequences. A number of generic Markov chain Monte Carlo (MCMC) proposal moves are described, and the calculation of their proposal ratios is illustrated.
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AbstractThis chapter provides a detailed introduction to modern Bayesian computation. The Metropolis–Hastings algorithm is illustrated using a simple example of distance estimation between two sequences. A number of generic Markov chain Monte Carlo (MCMC) proposal moves are described, and the calculation of their proposal ratios is illustrated.
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2004
We maintain that the analysis and synthesis of random fields is much faster in a hierarchical setting. In particular, complicated long-range interactions at a fine scale become progressively more local (and therefore more efficient) at coarser levels. The key to effective coarse-scale activity is the proper model definition at those scales. This can be
openaire +1 more source
We maintain that the analysis and synthesis of random fields is much faster in a hierarchical setting. In particular, complicated long-range interactions at a fine scale become progressively more local (and therefore more efficient) at coarser levels. The key to effective coarse-scale activity is the proper model definition at those scales. This can be
openaire +1 more source