Approximating Integrals via Monte Carlo and Deterministic Methods

Approximating Integrals via Monte Carlo and Deterministic Methods

by Michael Evans, Tim Swartz, T. Swartz
     
 

ISBN-10: 0198502788

ISBN-13: 9780198502784

Pub. Date: 06/28/2000

Publisher: Oxford University Press, USA

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,

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Overview

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.

Product Details

ISBN-13:
9780198502784
Publisher:
Oxford University Press, USA
Publication date:
06/28/2000
Series:
Oxford Statistical Science Series, #20
Edition description:
New Edition
Pages:
304
Product dimensions:
6.20(w) x 9.30(h) x 0.90(d)

Table of Contents

1. Introduction
2. Some basic concepts
3. Algorithms for sampling
4. Asymptotic approximations
5. Multiple quadrature
6. Independent importance sampling
7. Markov Chain methods References Author index Subject index

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