Monte Carlo strategies in scientific computing / Jun S. Liu.
"This book provides a self-contained and up-to-date treatment of the Monte Carlo method and develops a common framework under which various Monte Carlo techniques can be "standardized" and compared. Given the interdisciplinary nature of the topics and a moderate prerequisite for the reader, this boo...
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| Main Author: | |
|---|---|
| Language: | English |
| Published: |
New York :
Springer,
[2001], ©2001.
|
| Series: | Springer series in statistics.
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| Subjects: | |
| Physical Description: | xvi, 343 pages : illustrations ; 24 cm. |
| Format: | Book |
MARC
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| 100 | 1 | |a Liu, Jun S. |0 http://id.loc.gov/authorities/names/n00016727 | |
| 245 | 1 | 0 | |a Monte Carlo strategies in scientific computing / |c Jun S. Liu. |
| 260 | |a New York : |b Springer, |c [2001], ©2001. | ||
| 300 | |a xvi, 343 pages : |b illustrations ; |c 24 cm. | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a unmediated |b n |2 rdamedia | ||
| 338 | |a volume |b nc |2 rdacarrier | ||
| 490 | 1 | |a Springer series in statistics | |
| 504 | |a Includes bibliographical references (pages 313-332) and indexes. | ||
| 505 | 0 | |a Preface -- 1. Introduction and examples -- 2. Basic principles : rejection, weighting, and others -- 3. Theory of sequential Monte Carlo -- 4. Sequential Monte Carlo in action -- 5. Metropolis algorithm and beyond -- 6. The Gibbs sampler -- 7. Cluster algorithms for the Ising model -- 8. General conditional sampling -- 9. Molecular dynamics and hybrid Monte Carlo -- 10. Multilevel sampling and optimization methods -- 11. Population-based Monte Carlo methods -- 12. Markov chains and their convergence -- 13. Selected theoretical topics -- A. Basics in probability and statistics -- References -- Author index -- Subject index. | |
| 520 | 1 | |a "This book provides a self-contained and up-to-date treatment of the Monte Carlo method and develops a common framework under which various Monte Carlo techniques can be "standardized" and compared. Given the interdisciplinary nature of the topics and a moderate prerequisite for the reader, this book should be of interest to a broad audience of quantitative researchers such as computational biologists, computer scientists, econometricians, engineers, probabilists, and statisticians. It can also be used as the textbook for a graduate-level course on Monte Carlo methods. Many problems discussed in the later chapters can be potential thesis topics for master's or Ph. D. students in statistics or computer science departments."--Jacket. | |
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