rdf-syntax-grammar
mods:
  schema location: http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
  title info:
    title: Handbook of monte carlo methods
  name:
    name part: Kroese, Dirk P.
    role:
      role term: creator
  name:
    name part: Taimre, Thomas.
    role:
      role term: aut
  name:
    name part: Botev, Zdravko I.
    role:
      role term: aut
  type of resource: text
  genre: bibliography
  genre: Electronic resource.
  genre: Electronic books.
  genre: Handbooks and manuals.
  origin info:
    place:
      place term: xx
    date issued: 2011
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: remote
    extent: 1 online resource (768 pages).
  abstract: A comprehensive overview of Monte Carlo simulation that explores the latest topics, techniques, and real-world applications. More and more of today's numerical problems found in engineering and finance are solved through Monte Carlo methods. The heightened popularity of these methods and their continuing development makes it important for researchers to have a comprehensive understanding of the Monte Carlo approach. Handbook of Monte Carlo Methods provides the theory, algorithms, and applications that helps provide a thorough understanding of the emerging dynamics of this rapidly-growing field. The authors begin with a discussion of fundamentals such as how to generate random numbers on a computer. Subsequent chapters discuss key Monte Carlo topics and methods, including: Random variable and stochastic process generation, Markov chain Monte Carlo, featuring key algorithms such as the Metropolis-Hastings method, the Gibbs sampler, and hit-and-run, Discrete-event simulation, Techniques for the statistical analysis of simulation data including the delta method, steady-state estimation, and kernel density estimation, Variance reduction, including importance sampling, latin hypercube sampling, and conditional Monte Carlo, Estimation of derivatives and sensitivity analysis. Advanced topics including cross-entropy, rare events, kernel density estimation, quasi Monte Carlo, particle systems, and randomized optimization. The presented theoretical concepts are illustrated with worked examples that use MATLAB® a related Web site houses the MATLAB® code, allowing readers to work hands-on with the material. Detailed appendices provide background material on probability theory, stochastic processes, and mathematical statistics as well as the key optimization concepts and techniques that are relevant to Monte Carlo simulation.
  table of contents: Cover13; -- Contents -- Preface -- Acknowledgments -- 1 Uniform Random Number Generation -- 1.1 Random Numbers -- 1.1.1 Properties of a Good Random Number Generator -- 1.1.2 Choosing a Good Random Number Generator -- 1.2 Generators Based on Linear Recurrences -- 1.2.1 Linear Congruential Generators -- 1.2.2 Multiple-Recursive Generators -- 1.2.3 Matrix Congruential Generators -- 1.2.4 Modulo 2 Linear Generators -- 1.3 Combined Generators -- 1.4 Other Generators -- 1.5 Tests for Random Number Generators -- 1.5.1 Spectral Test -- 1.5.2 Empirical Tests -- References -- 2 Quasirandom Number Generation -- 2.1 Multidimensional Integration -- 2.2 Van der Corput and Digital Sequences -- 2.3 Halton Sequences -- 2.4 Faure Sequences -- 2.5 Sobol' Sequences -- 2.6 Lattice Methods -- 2.7 Randomization and Scrambling -- References -- 3 Random Variable Generation -- 3.1 Generic Algorithms Based on Common Transformations -- 3.1.1 Inverse-Transform Method -- 3.1.2 Other Transformation Methods -- 3.1.3 Table Lookup Method -- 3.1.4 Alias Method -- 3.1.5 Acceptance-Rejection Method -- 3.1.6 Ratio of Uniforms Method -- 3.2 Generation Methods for Multivariate Random Variables -- 3.2.1 Copulas -- 3.3 Generation Methods for Various Random Objects -- 3.3.1 Generating Order Statistics -- 3.3.2 Generating Uniform Random Vectors in a Simplex -- 3.3.3 Generating Random Vectors Uniformly Distributed in a Unit Hyperball and Hypersphere -- 3.3.4 Generating Random Vectors Uniformly Distributed in a Hyperellipsoid -- 3.3.5 Uniform Sampling on a Curve -- 3.3.6 Uniform Sampling on a Surface -- 3.3.7 Generating Random Permutations -- 3.3.8 Exact Sampling From a Conditional Bernoulli Distribution -- References -- 4 Probability Distributions -- 4.1 Discrete Distributions -- 4.1.1 Bernoulli Distribution -- 4.1.2 Binomial Distribution -- 4.1.3 Geometric Distribution -- 4.1.4 Hypergeometric Distribution -- 4.1.5 Negative Binomial Distribution -- 4.1.6 Phase-Type Distribution (Discrete Case) -- 4.1.7 Poisson…
  note: Dirk P. Kroese, Thomas Taimre, Zdravko I. Botev.
  note: Includes bibliographical references and index.
  subject:
    topic: Monte Carlo method
    genre: Handbooks, manuals, etc
  subject:
    topic: Monte Carlo method
  subject:
    topic: MATHEMATICS
    topic: Probability & Statistics
    topic: General
  subject:
    topic: MATHEMATICS
    topic: Numerical Analysis
  subject:
    topic: Monte Carlo method
  classification: QA298 .K76 2011
  classification: 518/.282
  classification: MAT029000
  related item:
    title info:
      title: Handbook of Monte Carlo methods
    name:
      name part: Kroese, Dirk P.
    origin info:
      publisher: Hoboken, N.J. : Wiley, ©2011
    identifier: (DLC) 2010042348
    identifier: (OCoLC)669751136
  related item:
    title info:
      title: Wiley series in probability and statistics ; 706
  identifier: 9781118014967
  identifier: 1118014960
  identifier: 9781118014943
  identifier: 1118014944
  identifier: 9780470177938
  identifier: 0470177934
  identifier: 307242 MIL
  identifier: https://doi.org/10.1002/9781118014967
  record info:
    record content source: IDEBK
    record creation date: 110627
    record change date: 20170124070549.1
    record identifier: ocn743404801
    language of cataloging:
      language term: eng