„StatisticiansKroese, Thomas Taimre (both U. of Queensland), and Zdravko I. Botev(U. of Montreal)
offerresearchers and graduate and advanced graduate students acompendium of Monte Carlo
methods, which are statistical methods that involve random experiments on acomputer. There are a
greatmany such methods being used for so many kinds of problems in somany fields that such an
overallview is hard to find. Combining theory, algorithms, andapplications, they consider such topics
asuniform random number generation, probability distributions, discrete event simulation, variance
reduction, estimating derivatives, and applications to networkreliability.“ (Annotation 2011 Book News
Inc. Portland, OR)
A comprehensive overview of Monte Carlo simulation that exploresthe latest topics, techniques, and real-world applications
More and more of today's numerical problems found inengineering and finance are solved through Monte Carlo methods. Theheightened popularity of these methods and their continuingdevelopment makes it important for researchers to have acomprehensive understanding of the Monte Carlo approach. Handbook of Monte Carlo Methods provides the theory, algorithms, and applications that helps provide a thoroughunderstanding of the emerging dynamics of this rapidly-growingfield.
The authors begin with a discussion of fundamentals such as howto generate random numbers on a computer. Subsequent chaptersdiscuss key Monte Carlo topics and methods, including:
* Random variable and stochastic process generation
* Markov chain Monte Carlo, featuring key algorithms such as theMetropolis-Hastings method, the Gibbs sampler, and hit-and-run
* Discrete-event simulation
* Techniques for the statistical analysis of simulation dataincluding the delta method, steady-state estimation, and kerneldensity estimation
* Variance reduction, including importance sampling, latinhypercube sampling, and conditional Monte Carlo
* Estimation of derivatives and sensitivity analysis
* Advanced topics including cross-entropy, rare events, kerneldensity estimation, quasi Monte Carlo, particle systems, andrandomized optimization
The presented theoretical concepts are illustrated with workedexamples that use MATLAB¯®, a related Web sitehouses the MATLAB¯® code, allowing readers to workhands-on with the material and also features the author's ownlecture notes on Monte Carlo methods. Detailed appendices providebackground material on probability theory, stochastic processes, and mathematical statistics as well as the key optimizationconcepts and techniques that are relevant to Monte Carlosimulation.
Handbook of Monte Carlo Methods is an excellent referencefor applied statisticians and practitioners working in the fieldsof engineering and finance who use or would like to learn how touse Monte Carlo in their research. It is also a suitable supplementfor courses on Monte Carlo methods and computational statistics atthe upper-undergraduate and graduate levels.
More and more of today's numerical problems found inengineering and finance are solved through Monte Carlo methods. Theheightened popularity of these methods and their continuingdevelopment makes it important for researchers to have acomprehensive understanding of the Monte Carlo approach. Handbook of Monte Carlo Methods provides the theory, algorithms, and applications that helps provide a thoroughunderstanding of the emerging dynamics of this rapidly-growingfield.
The authors begin with a discussion of fundamentals such as howto generate random numbers on a computer. Subsequent chaptersdiscuss key Monte Carlo topics and methods, including:
* Random variable and stochastic process generation
* Markov chain Monte Carlo, featuring key algorithms such as theMetropolis-Hastings method, the Gibbs sampler, and hit-and-run
* Discrete-event simulation
* Techniques for the statistical analysis of simulation dataincluding the delta method, steady-state estimation, and kerneldensity estimation
* Variance reduction, including importance sampling, latinhypercube sampling, and conditional Monte Carlo
* Estimation of derivatives and sensitivity analysis
* Advanced topics including cross-entropy, rare events, kerneldensity estimation, quasi Monte Carlo, particle systems, andrandomized optimization
The presented theoretical concepts are illustrated with workedexamples that use MATLAB¯®, a related Web sitehouses the MATLAB¯® code, allowing readers to workhands-on with the material and also features the author's ownlecture notes on Monte Carlo methods. Detailed appendices providebackground material on probability theory, stochastic processes, and mathematical statistics as well as the key optimizationconcepts and techniques that are relevant to Monte Carlosimulation.
Handbook of Monte Carlo Methods is an excellent referencefor applied statisticians and practitioners working in the fieldsof engineering and finance who use or would like to learn how touse Monte Carlo in their research. It is also a suitable supplementfor courses on Monte Carlo methods and computational statistics atthe upper-undergraduate and graduate levels.
