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Monte Carlo Statistical Methods,Used
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Monte Carlo statistical methods, particularly those based on Markov chains, have now matured to be part of the standard set of techniques used by statisticians. This book is intended to bring these techniques into the classroom, being a selfcontained logical development of the subject. This is a textbook intended for a second year graduate course. We do not assume that the reader has any familiarity with Monte Carlo techniques (such as random variable generation), or with any Markov chain theory. Chapters 13 are introductory, first reviewing various statistical methodologies, then covering the basics of random variable generation and Monte Carlo integration. Chapter 4 is an introduction to Markov chain theory, and Chapter 5 provides the first application of Markov chains to optimization problems. Chapters 6 and 7 cover the heart of MCMC methodology, the MetropolisHastings algorithm and the Gibbs sampler. Finally, Chapter 8 presents methods for monitoring convergence of the MCMC methods, while Chapter 9 shows how these methods apply to some statistical settings which cannot be processed otherwise. Each chapter concludes with a section of notes that serve to enhance the discussion in the chapters. 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. In addition to many papers on Bayesian statistics, simulation, and decision theory, he has written three other books, including The Bayesian Choice, Springer 1994. He also edited Discretization and MCMC Convergence Assessment, Springer 1998. He has served as 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 Soci??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 Cornel University. He is active in many aspects on both theoretical and applied statistics, and 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, 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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- Q: How many pages does this book have? A: This book contains five hundred twenty-eight pages. It's a comprehensive resource for understanding Monte Carlo statistical methods.
- Q: What is the binding type of this book? A: The book is bound in hardcover. This ensures durability and makes it suitable for frequent use in academic settings.
- Q: What are the dimensions of this book? A: The book's dimensions are six point twenty-six inches in length, one point eighteen inches in width, and nine point forty-nine inches in height. These dimensions make it portable for students.
- Q: What is the intended reading level for this book? A: This book is intended for second-year graduate students. It does not assume prior knowledge of Monte Carlo techniques or Markov chain theory.
- Q: How do I apply the methods learned in this book? A: You can apply the methods by following the structured chapters that guide you through theoretical concepts and practical applications. Each chapter includes exercises for better understanding.
- Q: Is this book suitable for beginners in statistics? A: No, this book is not suitable for beginners. It is designed for those with some background in statistics, particularly graduate students.
- Q: How do I keep this book in good condition? A: To keep the book in good condition, store it upright on a shelf and avoid exposing it to moisture. Regular dusting will also help maintain its appearance.
- Q: What should I do if the book arrives damaged? A: If the book arrives damaged, you should contact the seller for a return or exchange. Most sellers have a customer service policy for such issues.
- Q: Can I share this book with a classmate? A: Yes, you can share this book with a classmate. However, keep in mind that it's a comprehensive textbook, so both of you might benefit from having individual copies for reference.
- Q: What kind of statistical methodologies are covered in this book? A: The book covers various statistical methodologies, including random variable generation and Monte Carlo integration. It is a thorough introduction to Monte Carlo methods.
- Q: Is there a focus on practical applications in this book? A: Yes, there is a strong focus on practical applications throughout the chapters. Each concept is linked to real-world statistical problems.
- Q: Who are the authors of this book? A: The authors of this book are Christian P. Robert and George Casella. Both are well-respected figures in the field of statistics.
- Q: Does this book include exercises or problems to solve? A: Yes, each chapter concludes with a section of notes and often includes exercises to reinforce the concepts discussed.
- Q: Can this book be used for self-study? A: Yes, this book can be used for self-study. It is designed to be self-contained, making it suitable for independent learners.
- Q: Is this book relevant for current statistical practices? A: Yes, this book is highly relevant for current statistical practices, especially in the field of Monte Carlo methods and Markov chains.