Approximating Integrals Via Monte Carlo and Deterministic Methods

Klappentext This book is designed to introduce graduate students and researchers to the primary methods useful for approximating integrals. The emphasis is on those methods that have been found to be of practical use, focusing on approximating higher- dimensional integrals with coverage of the lower-dimensional case as well. Included in the book are asymptotic techniques, multiple quadrature and quasi-random techniques and a complete development of Monte Carlo algorithms. For the Monte Carlo section important sampling methods, variance reduction techniques and the primary Markov Chain Monte Carlo algorithms are covered. This book brings these various techniques together for the first time, and provides an accessible textbook and reference for researchers in a wide variety of disciplines. Zusammenfassung This text is designed to introduce graduate students and researchers to the primary methods useful for approximating integrals. The emphasis is on those methods that have been found to be of practical use, with the focus on approximating higher-dimensional integrals although lower is also covered. Inhaltsverzeichnis 1: Introduction 2: Some basic tools 3: Algorithms for sampling from distributions 4: Approximating integrals via asymptotics 5: Multiple quadrature 6: Importance sampling 7: Markov Chain methods

Dettagli sul prodotto

• Autori: Michael Evans, Michael (Professor of Statistics Evans, Michael Swartz Evans, Tim Swartz, Timothy Swartz, Swartz Timothy
• Editore: Oxford University Press
• Lingue: Inglese
• Formato: Copertina rigida
• Pubblicazione: 23.03.2000
• EAN: 9780198502784
• ISBN: 978-0-19-850278-4
• Pagine: 298
• Serie: Oxford Statistical Science Series; Oxford Statistical Science Ser; Oxford Statistical Science Series; Oxford Statistical Science
• Categorie: Scienze naturali, medicina, informatica, tecnica > Scienze naturali, tematiche generali; COMPUTERS / Computer Science, MATHEMATICS / Probability & Statistics / General, TECHNOLOGY & ENGINEERING / Quality Control, TECHNOLOGY & ENGINEERING / Industrial Engineering, BUSINESS & ECONOMICS / Econometrics, computer science, Economic statistics, Probability & statistics, Reliability engineering, Probability and statistics, Econometrics and economic statistics