Approximating integrals via Monte Carlo and deterministic methods
โ Scribed by Michael Evans, Tim Swartz
- Book ID
- 127422572
- Publisher
- Oxford University Press
- Year
- 2000
- Tongue
- English
- Weight
- 3 MB
- Series
- Oxford statistical science series 20
- Edition
- 1st
- Category
- Library
- City
- Oxford; New York
- ISBN-13
- 9780198502784
No coin nor oath required. For personal study only.
โฆ Synopsis
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.
๐ SIMILAR VOLUMES
In the context of Bayesian non-parametric statistics, the distribution of a stochastic process serves as a prior over the class of functions indexed by its sample paths. Dykstra and Laud (1981) defined a stochastic process whose sample paths can be used to index monotone hazard rates. Although they