Until the advent of powerful and accessible computing methods, the experimenter was often confronted with a difficult choice. Either describe an accurate model of a phenomenon, which would usually preclude the computation of explicit answers, or choose a standard model which would allow this computation, but may not be a close representation of a realistic model. This dilemma is present in many branches of statistical applications, for example in electrical engineering, aeronautics, biology, networks, and astronomy. Markov chain Monte Carlo methods have been developed to provide realistic models.

About the Author:

Christian P. Robert is Professor of Statistics in the Mathematics Department at the University of Rouen, France. He is also Head of the Statistics Laboratory at the Center for Research in Economics and Statistics (CREST) of the National Institute for Statistics and Economic Studies (INSEE) in Paris, and lecturer at Ecole Polytechnique. He has written three other books, including The Bayesion Choice, Springer, 1994. He also edited Discretization and MCMC Convergence Assessment, Springer, 1998. He has served as an associate editor for the Annals of Statistics and the Journal of the American Statistical Association. He is a fellow of the Institute of Mathematical Statistics, and a winner of the Young Statistician Award of the So66t6 de Statistique de Paris in 1995. George Casella is the liberty Hyde Bailey Professor of Biological Statistics in the College of Agriculture and life Sciences at Cornell University. He has served as the Theory and Methods Editor of the Journal of the American Statistical Association. He has authored three other textbooks: Statistical Inference, 1990, with Roger L. Berger; Variance Components, 1992, with Shayle R. Searle and Charles E. McCulloch; and Theory of Point Estimation, Second Edition, 1998, with Erich Lehmann. He is a fellow of the Institute of Mathematical Statistics and the American Statistical Association, and an elected fellow of the International Statistical Institute.

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