Monte Carlo Statistical Methods (Springer Texts in Statistics)

Robert, Christian P.; Casella, G.C.

ISBN 10: 038798707X / ISBN 13: 9780387987071

Verlag: Springer-Verlag New York Inc., 1999

Beschreibung

gift inscription on flyleaf, light wear around edges of cover. Bestandsnummer des Verkäufers 406

Bibliografische Details

Titel: Monte Carlo Statistical Methods (Springer ...

Verlag: Springer-Verlag New York Inc.

Erscheinungsdatum: 1999

Einband: Hardcover

Zustand: Good

Über diesen Titel

Inhaltsangabe:

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. Written as a self-contained logical development of the subject, this book will be suitable as an introduction to the field or as a textbook intended for a seconcl-year graduate course. The reader is not assumed to have any familiarity with Monte Carlo techniques (such as random variable generation), or with any Markov chain theory. Chapters 1 to 3 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 Metropolis-Hastings 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 that cannot be processed otherwise. Each chapter concludes with a section of notes that serve to enhance the discussion in the chapters.

Reseña del editor:

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.