MARKOV CHAIN MONTE CARLO
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MARKOV CHAIN MONTE CARLO
Innovations and Applications
by W S Kendall (University of Warwick, UK), F Liang (Texas A&M University, USA) & J-S Wang (National University of Singapore, Singapore)
Markov Chain Monte Carlo (MCMC) originated in statistical physics, but has spilled over into various application areas, leading to a corresponding variety of techniques and methods. That variety stimulates new ideas and developments from many different places, and there is much to be gained from cross-fertilization. This book presents five expository essays by leaders in the field, drawing from perspectives in physics, statistics and genetics, and showing how different aspects of MCMC come to the fore in different contexts. The essays derive from tutorial lectures at an interdisciplinary program at the Institute for Mathematical Sciences, Singapore, which exploited the exciting ways in which MCMC spreads across different disciplines.
Contents:
- Introduction to Markov Chain Monte Carlo Simulations and Their Statistical Analysis (B A Berg)
- An Introduction to Monte Carlo Methods in Statistical Physics (D P Landau)
- Notes on Perfect Simulation (W S Kendall)
- Sequential Monte Carlo Methods and Their Applications (R Chen)
- MCMC in the Analysis of Genetic Data on Pedigrees (E A Thompson)
View Full Text (4,397 KB)
Readership: Academic researchers in physics, statistics and
bioinformatics.
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240pp
Pub. date: Nov 2005
eISBN 978-981-270-091-9
US$88
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