Approximating Integrals via Monte Carlo and Deterministic Methods
Series: Oxford Statistical Science Series; 20;
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69 982 Ft
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Product details:
- Publisher OUP Oxford
- Date of Publication 23 March 2000
- Number of Volumes laminated boards
- ISBN 9780198502784
- Binding Hardback
- No. of pages298 pages
- Size 241x161x21 mm
- Weight 559 g
- Language English 0
Categories
Short description:
Integrals are one of the primary computational tools in mathematics, and hence are of great importance in just about any numerate discipline, including statistics, mathematical finance, computer science and engineering. Although it is occasionally possible to compute integrals exactly this is typically not the case. In these situations it becomes necessary to approximate integrals. This book covers all the most useful approximation techniques so far discovered; the first time that all such techniques have been included in a single book and at a level accessible for students. In particular, it includes a complete development of the material needed to construct the highly popular Markov Chain Monte Carlo (MCMC) methods.
MoreLong description:
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, and although the focus is on approximating higher- dimensional integrals the lower-dimensional case is also covered. Included in the book are asymptotic techniques, multiple quadrature and quasi-random techniques as well as a complete development of Monte Carlo algorithms. For the Monte Carlo section importance 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 hence provides an accessible textbook and reference for researchers in a wide variety of disciplines.
MoreTable of Contents:
Introduction
Some basic tools
Algorithms for sampling from distributions
Approximating integrals via asymptotics
Multiple quadrature
Importance sampling
Markov Chain methods
