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Monte Carlo Methods
Abstract
This introduction to Monte Carlo methods seeks to identify and study the unifying elements that underlie their effective application. Initial chapters provide a short treatment of the probability and statistics needed as background, enabling those without experience in Monte Carlo techniques to apply these ideas to their research.The book 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 modeling of radiation transport arises directly from a schematic probabilistic description of the interaction of radiation with matter. Building on this example, the relationship between random walks and integral equations is outlined
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Cited by
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An introduction to MCMC for machine learning.
Andrieu, C., de Freitas, N., Doucet, A., & Jordan, M. I. (2003) · Machine Learning · doi:10.1023/a:1020281327116 · Cited by 2,451
The theory of variational hybrid quantum-classical algorithms
McClean, J. R., Romero, J., Babbush, R., & Aspuru-Guzik, A. (2016) · New Journal of Physics · doi:10.1088/1367-2630/18/2/023023 · Cited by 2,227
Optimal structure, market dynamism, and the strategy of simple rules
Davis, J. P., Eisenhardt, K. M., & Bingham, C. B. (2009) · Administrative Science Quarterly · doi:10.2189/asqu.2009.54.3.413 · Cited by 804
Metadata
- Authors
- Kalos, M. H., & Whitlock, P. A.
- Venue
- Wiley eBooks
- Publisher
- Wiley
- Published
- 1986-12-03
- Pages
- 128-139
- OpenAlex
- W3143893444
- Cited by
- 1,546
- References
- 0
- FWCI
- 9.00
- Type
- book
- Language
- en
- Open access
- closed
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