"Although the book is written as a textbook, with many carefully worked out examples and exercises, it will be very useful for the researcher since the authors discuss their favorite research topics (Monte Carlo optimization and convergence diagnostics) going through many relevant referencesThis book is a comprehensive treatment of the subject and will be an essential reference for statisticians working with McMC." -Mathematical Reviews

Very popular book published in 1999 New advances are covered in the second edition Request lecturer material: sn.pub/lecturer-material

Auteur
From the reviews of the first edition:

"Although the book is written as a textbook, with many carefully worked out examples and exercises, it will be very useful for the researcher since the authors discuss their favorite research topics (Monte Carlo optimization and convergence diagnostics) going through many relevant references...This book is a comprehensive treatment of the subject and will be an essential reference for statisticians working with McMC." -Mathematical Reviews

Contenu
1 Introduction.- 2 Random Variable Generation.- 3 Monte Carlo Integration.- 4 Controling Monte Carlo Variance.- 5 Monte Carlo Optimization.- 6 Markov Chains.- 7 The MetropolisHastings Algorithm.- 8 The Slice Sampler.- 9 The Two-Stage Gibbs Sampler.- 10 The Multi-Stage Gibbs Sampler.- 11 Variable Dimension Models and Reversible Jump Algorithms.- 12 Diagnosing Convergence.- 13 Perfect Sampling.- 14 Iterated and Sequential Importance Sampling.- A Probability Distributions.- B Notation.- B.1 Mathematical.- B.2 Probability.- B.3 Distributions.- B.4 Markov Chains.- B.5 Statistics.- B.6 Algorithms.- References.- Index of Names.- Index of Subjects.