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A First Course in Monte Carlo, by George Fishman
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A FIRST COURSE IN MONTE CARLO shows you how to design, perform, and analyze the results of MC experiments based on independent replications, Markov chain MC, and MC optimization. The text emphasizes the variance-reducing techniques of importance sampling, stratified sampling, Rao-Blackwellization, control variates, antithetic variates, and quasi-random numbers. For solving optimization problems it describes several MC techniques, including simulated annealing, simulated tempering, swapping, stochastic tunneling, and genetic algorithms. Examples from many areas show how these techniques perform in practice. Hands-on exercises allow you to experience challenges encountered when solving real problems. An answer key to selected problems is included.
- Sales Rank: #2471444 in Books
- Brand: Brand: Cengage Learning
- Published on: 2005-10-05
- Original language: English
- Number of items: 1
- Dimensions: 9.54" h x .85" w x 7.46" l, 1.81 pounds
- Binding: Hardcover
- 350 pages
- Used Book in Good Condition
About the Author
George Fishman has regularly contributed to the literature on the Monte Carlo method and discrete-event simulation over the last 40 years. His earlier book, MONTE CARLO: CONCEPTS, ALGORITHMS, AND APPLICATIONS (Springer-Verlag, 1996) won the 1996 Lancaster award for best publication of the Institute for Operations Research and Management Sciences (INFORMS) and the 1997 outstanding publication award of the INFORMS College on Simulation.
Most helpful customer reviews
11 of 11 people found the following review helpful.
A very good first introduction to Monte Carlo
By Todd Ebert
A first course in Monte Carlo offers an introduction to Monte Carlo sampling, a very important topic in both statistics and computer science. Monte Carlo sampling can be described as follows. Suppose one has a function f(x) that is defined over a state space X. Two common problems are to
i) determine the integeral/expectancy of f over the state space, and
ii) find a value x* in the space for which f(x*)
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