## by George Fishman

ISBN: 053442046X
ISBN13: 978-0534420468
Language: English
Publisher: Duxbury Press; 1 edition (October 5, 2005)
Pages: 350
Category: Engineering
Subategory: Building

ePub size: 1966 kb
Fb2 size: 1594 kb
DJVU size: 1433 kb
Rating: 4.2
Other Formats: lit docx doc mbr

For professional mathematical scientists and engineers this book provides a ready reference to the Monte Carlo method, especially to implementatable algorithms for performing sampling experiments on a computer and for analyzing their results.

A First Course in Monte . .has been added to your Cart. In this text Fishman presents how to approximate the solutions to both problems via Monte Carlo sampling; both independent Monte Carlo and Markov chain Monte Carlo sampling. In this text Fishman presents how to approximate the solutions to both problems via Monte Carlo sampling; both independent Monte Carlo and Markov chain Monte Carlo sampling,xn and then . Statistically one can show that the average will converge to the desired value as n is allowed to increase.

Fishman’s book is a welcome addition on this revolutionary topic. A First Course in Monte Carlo (henceforth AFCMC) distinguishes itself from the crowded area of excellent books on Monte Carlo (. Chen, Shao, and Ibrahim 2000; Fishman 1997; Liu 2001; Robert and Casella 1999) and computational statistics (. Givens and Hoeting 2005; Lange 1999) by being a dedicated textbook.

This book provides an introduction to the Monte Carlo method suitable for a one-or two-semester course for graduate and . Errors, both statistical and computational, abound in every Monte Carlo sam pling experiment, and a considerable methodology exists for controlling them.

This book provides an introduction to the Monte Carlo method suitable for a one-or two-semester course for graduate and advanced undergraduate students in the mathematical and engineering sciences. It also can serve as a reference for the professional analyst. In the past, my inability to provide students with a single source book on this topic for class and for later professional reference had left me repeatedly frustrated, and eventually motivated me to write this book.

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

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.

A First Course in Monte Carlo. george Fishman," Journal of the American Statistical Association, American Statistical Association, vol. 102, pages 758-758, June. Handle: RePEc:bes:jnlasa:v:102:y:2007:m:june:p:758-758. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

ble publication of his latest book, A First Course in Monte Carlo (Fishman 2005). George Fishman, is the preeminent expert on simulation and Monte Carlo, and I encour-. age you to look at his books, Monte Carlo: Concepts, Algorithms, and Applications and. Sandy Stidham succeeded George as department chair in 1990. Discrete-Event Simulation: Modeling, Programming, and Analysis. During our mutual retirement, George and I have made a habit of going out for coffee.

More by George Fishman. The African American Struggle for Freedom and Equality: The Development of a People's Identity, New Jersey, 1624-1850 (Studies in African American History and Culture).

Discover Book Depository's huge selection of George Fishman books online. Free delivery worldwide on over 20 million titles. A First Course in Monte Carlo Simulation. The African American Struggle for Freedom and Equality.

Поиск книг BookFi BookSee - Download books for free. George Washington: Foundation of Presidential Leadership and Character. Monte-Carlo methods, concepts and applications (Springer)(722s). Ethan Fishman, William D. Pederson, Mark J. Rozell. 3 Mb. Judaism and Modernization on the Religious Kibbutz. 6 Mb. Monte-Carlo: concepts, algorithms, and applications. Категория: Mathematics, Probability, Statistics and applications.

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.
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*) <= f(x) for all x in X.

In this text Fishman presents how to approximate the solutions to both problems via Monte Carlo sampling; both independent Monte Carlo and Markov chain Monte Carlo sampling. Independent Monte Carlo sampling involves using a probability distribution w(x) over X to randomly generate n sample points x1,...,xn and then e.g. approximate the expectancy of f over the space by averaging the values
(f/w)(x1),...,(f/w)(xn). Statistically one can show that the average will converge to the desired value as n is allowed to increase. Chapter 2 discusses this method in good detail. I especially appreciated his discussion on the cost of generating a sample, along with the variance of the function values evaluated at the sample points, and the trade-off that ensues when one attempts to reduce variance (i.e. obtain faster convergence) at a cost of a more costly sampling technique. He then goes on to state a very useful fact with respect to two different sampling plans, in that the better plan will induce CR * VR < 1, where CR and VR are the cost and variance ratios of the two plans (I'm assuming the better plan is in the numerator of both ratios). This result applies to both problems i) and ii) above, and can be used as a means for testing the merit of a new sampling/optimization technique relative to some existing one.

Chapters 3 and 4 are very well done, and involve techniques for random-number generators and random-variable generators, the latter being essential for implementing either Monte Carlo technique on a computer. I especially was grateful for his discussion in Chapter 4 of how good generators generate values that uniformly fill a k-dimensional lattice, with each lattice component being an element from the set
{0,1/m,2/m,...(m-1)/m} which is the set of possible integer outputs of the generator. It helped me better understand why the Mersenne Twister generator is so highly regarded in practice.

Chapters 6-9 cover the Markov-Chain Monte Carlo method of sampling points in the state space. The basic idea is that now the sample points are DEPENDENT and forming a Markov chain according to some transition kernel. Good coverage is given to the Hastings-Metropolis algorithm which comes in very handy when the distribution w(x) that one needs to use for sampling is only partially known. The HM algorithm allows one to generate dependent sample paths whose elements converge to the w(x) distribution, yet without fully knowing the distribution w(x)! (usually one is only required to know the distribution up to a multiplicative constant).

In Chapter 8, the HM algorithm is applied to the theory of Simulated Annealing. And although the full implementation details seemed somewhat sketchy, the presentation succeeded in providing a good introduction to the theory behind this extremely important optimization technique.

Finally, this book has several good application examples provided in each chapter. My only minor criticism is that more basic examples would have helped in understanding some of the more advanced material. But all-in all this book will serve as a very usefull reference for years to come.

Horrible, I waited a month to get this book after I placed the order and I never received this book. I tried communicating with the seller on the status of my order and never heard back. Horrible service!!!!!!!!!!!!!!!

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