Monte Carlo Methods - ISBN:0471898392

Monte Carlo Methods

Date: 06 May 2011, 20:17

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This introduction to Monte Carlo Methods seeks to identify and study the unifying elements that underlie their effective application. It focuses on two basic themes. The first is the importance of random walks as they occur both in natural stochastic systems and in their relationship to integral and differential equations. The second theme is that of variance reduction in general and importance sampling in particular as a technique for efficient use of the methods. Random walks are introduced with an elementary example in which the modelling of radiation transport arises directly from a schematic probabilistic description of the interaction of radiation with matter. Building on that example, the relationship between random walks and integral equations is outlined. The applicability of these ideas to other problems is shown by a clear and elementary introduction to the solution of the Schrodinger equation by random walks.
The detailed discussion of variance reduction includes Monte Carlo evaluation of finite-dimensional integrals. Special attention is given to importance sampling, partly because of its intrinsic interest in quadrature, partly because of its general usefulness in the solution of integral equations. One significant feature is that Monte Carlo Methods treats the ?Metropolis algorithm? in the context of sampling methods, clearly distinguishing it from importance sampling.
Physicists, chemists, statisticians, mathematicians, and computer scientists will find Monte Carlo Methods a complete and stimulating introduction.
The publisher, John Wiley & Sons
An introduction to Monte Carlo methods and their applications that emphasizes the unifying ideas that underlie the study and use of good methods. Discusses the importance of random walks both as they occur in natural stochastic systems, and in their relation to integral and differential equations. In addition, variance reduction and importance sampling as a technical means of achieving variance reduction are addressed.
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