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Status:
Available4.5
16 reviewsThe book begins with a modernized introduction that addresses the basic concepts of probability, Markov processes, and convex optimization. Subsequent chapters discuss the dramatic changes that have occurred in the field of the Monte Carlo method, with coverage of many modern topics including:
Markov Chain Monte Carlo
Variance reduction techniques such as the transform likelihood ratio method and the screening method
The score function method for sensitivity analysis
The stochastic approximation method and the stochastic counter-part method for Monte Carlo optimization
The cross-entropy method to rare events estimation and combinatorial optimization
Application of Monte Carlo techniques for counting problems, with an emphasis on the parametric minimum cross-entropy method
An extensive range of exercises is provided at the end of each chapter, with more difficult sections and exercises marked accordingly for advanced readers. A generous sampling of applied examples is positioned throughout the book, emphasizing various areas of application, and a detailed appendix presents an introduction to exponential families, a discussion of the computational complexity of stochastic programming problems …
4.7
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