| 000 | 07883cam a2200733Mi 4500 | ||
|---|---|---|---|
| 001 | ocn743404801 | ||
| 003 | OCoLC | ||
| 005 | 20171116140421.0 | ||
| 006 | m o d | ||
| 007 | cr un|---uuuuu | ||
| 008 | 110627s2011 xx obf 001 0 eng d | ||
| 016 | 7 |
_a015684976 _2Uk |
|
| 020 |
_a9781118014967 _q(electronic bk.) |
||
| 020 |
_a1118014960 _q(electronic bk.) |
||
| 020 |
_a9781118014943 _q(electronic bk.) |
||
| 020 |
_a1118014944 _q(electronic bk.) |
||
| 020 |
_z9780470177938 _q(hardback) |
||
| 020 |
_z0470177934 _q(hardback) |
||
| 024 | 8 | _a9786613072429 | |
| 029 | 1 |
_aAU@ _b000048489625 |
|
| 029 | 1 |
_aDEBBG _bBV041911846 |
|
| 029 | 1 |
_aDEBSZ _b379320223 |
|
| 029 | 1 |
_aDEBSZ _b430994214 |
|
| 029 | 1 |
_aGBVCP _b790033895 |
|
| 029 | 1 |
_aHEBIS _b255170262 |
|
| 029 | 1 |
_aNLGGC _b333940105 |
|
| 029 | 1 |
_aNLGGC _b38961632X |
|
| 029 | 1 |
_aNZ1 _b14287772 |
|
| 029 | 1 |
_aNZ1 _b15340714 |
|
| 029 | 1 |
_aDEBBG _bBV043393335 |
|
| 035 |
_a(OCoLC)743404801 _z(OCoLC)720822762 _z(OCoLC)721356437 _z(OCoLC)770867378 _z(OCoLC)839304806 |
||
| 037 |
_a307242 _bMIL |
||
| 040 |
_aIDEBK _beng _epn _cIDEBK _dOCLCQ _dUKMGB _dDG1 _dOCLCQ _dOCLCF _dN$T _dCDX _dE7B _dYDXCP _dOSU _dEBLCP _dREDDC _dMERUC _dWAU _dDEBSZ _dOCL _dDEBBG _dOCLCQ _dCOO _dS3O _dOCLCQ |
||
| 049 | _aMAIN | ||
| 050 | 4 |
_aQA298 _b.K76 2011 |
|
| 072 | 7 |
_aMAT _x041000 _2bisacsh |
|
| 082 | 0 | 4 |
_a518/.282 _223 |
| 084 |
_aMAT029000 _2bisacsh |
||
| 100 | 1 | _aKroese, Dirk P. | |
| 245 | 1 | 0 |
_aHandbook of monte carlo methods / _cDirk P. Kroese, Thomas Taimre, Zdravko I. Botev. _h[electronic resource] |
| 260 |
_a[Place of publication not identified] : _bWiley, _c2011. |
||
| 300 | _a1 online resource (768 pages). | ||
| 336 |
_atext _btxt _2rdacontent |
||
| 337 |
_acomputer _bc _2rdamedia |
||
| 338 |
_aonline resource _bcr _2rdacarrier |
||
| 490 | 1 |
_aWiley series in probability and statistics ; _v706 |
|
| 504 | _aIncludes bibliographical references and index. | ||
| 505 | 0 | _aCover13; -- 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 Distribution -- 4.1.8 Uniform Distribution (Discrete Case) -- 4.2 Continuous Distributions -- 4.2.1 Beta Distribution -- 4.2.2 Cauchy Distribution -- 4.2.3 Exponential Distribution -- 4.2.4 F Distribution -- 4.2.5 Fr233;chet Distribution -- 4.2.6 Gamma Distribution -- 4.2.7 Gumbel Distribution -- 4.2.8 Laplace Distribution -- 4.2.9 Logistic Distribution -- 4.2.10 Log-Normal Distribution -- 4.2.11 Normal Distribution -- 4.2.12 Pareto Distribution -- 4.2.13 Phase-Type Distribution (Continuous Case) -- 4.2.14 Stable Distribution -- 4.2.15 Student's t Distribution -- 4.2.16 Uniform Distribution (Continuous Case) -- 4.2.17 Wald Distribution -- 4.2.18 Weibull Distribution -- 4.3 Multivariate Distributions -- 4.3.1 Dirichlet Distribution -- 4.3.2 Multinomial Distribution -- 4.3.3 Multivariate Normal Distribution -- 4.3.4 Multivariate Student's t Distribution -- 4.3.5 Wishart Distribution -- References -- 5 Random Process Generation -- 5.1 Gaussian Processes -- 5.1.1 Markovian Gaussian Processes -- 5.1.2 Stationary Gaussian Processes and the FFT -- 5.2 Markov Chains -- 5.3 Markov Jump Processes -- 5.4 Poisson Processes -- 5.4.1 Compound Poisson Process -- 5.5 Wiener Process and Brownian Motion -- 5.6 Stochastic Differential Eq. | |
| 520 | _aA 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. | ||
| 650 | 0 |
_aMonte Carlo method _vHandbooks, manuals, etc. |
|
| 650 | 4 | _aMonte Carlo method. | |
| 650 | 7 |
_aMATHEMATICS _xProbability & Statistics _xGeneral. _2bisacsh |
|
| 650 | 7 |
_aMATHEMATICS _xNumerical Analysis. _2bisacsh |
|
| 650 | 7 |
_aMonte Carlo method. _2fast _0(OCoLC)fst01025819 |
|
| 655 | 4 | _aElectronic resource. | |
| 655 | 4 | _aElectronic books. | |
| 655 | 7 |
_aHandbooks and manuals. _2fast _0(OCoLC)fst01423877 |
|
| 700 | 1 |
_aTaimre, Thomas. _4aut |
|
| 700 | 1 |
_aBotev, Zdravko I. _4aut |
|
| 776 | 0 | 8 |
_iPrint version: _aKroese, Dirk P. _tHandbook of Monte Carlo methods. _dHoboken, N.J. : Wiley, ©2011 _z9780470177938 _w(DLC) 2010042348 _w(OCoLC)669751136 |
| 830 | 0 |
_aWiley series in probability and statistics ; _v706. |
|
| 856 | 4 | 0 |
_uhttp://onlinelibrary.wiley.com/book/10.1002/9781118014967 _zWiley Online Library |
| 942 |
_2ddc _cBK |
||
| 999 |
_c205181 _d205181 |
||