Markov Chain Monte Carlo: Innovations and Applications (Lecture Notes Series, Institute for Mathematical Sciences, National University of Singapore)

Little more than a decade ago the term "genetic engineering" was hardly known outside research laboratories. Today it regularly makes headlines. Those in favor of genetic engineering--and those against it--tell us that it has the potential to change our lives perhaps more than any other scientific or technological advance. But what are the likely consequences of genetic engineering? Is it ethically acceptable? Should we be trying to improve on nature? In Improving Nature?, the authors, a biologist and a moral philosopher, examine the implications of genetic engineering in every aspect of our lives. The underlying science is clearly explained and the moral and ethical considerations are fully disussed, resulting in a wide-ranging, balanced overview of a controversial subject. Michael Jonathan Reiss, a biologist, is Professor of Science Education and Head of Science & Technology, University of London Institute of Education. He is the author of Understanding Science Lessons (Open University Press, 2000). Roger Straughan is Reader in Education at the University of Reading. He is the author of Beliefs, Behaviour and Education (Cassell Academic, 1989). Previous paperback edition (1996) 0-521-63754-6 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.--book Jacket. 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. Editors, W.s. Kendall, F. Liang, J.-s. Wang. Includes Bibliographical References And Index. 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. * 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) * Markov Chain Monte Carlo in the Analysis of Genetic Data on Pedigrees (E A Thompson)