| 000 | 05816cam a2200697Mu 4500 | ||
|---|---|---|---|
| 001 | ocn827207941 | ||
| 003 | OCoLC | ||
| 005 | 20171224114153.0 | ||
| 006 | m o d | ||
| 007 | cr cnu---unuuu | ||
| 008 | 130211s2012 enk o 000 0 eng d | ||
| 040 |
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| 020 |
_a9781118555286 _q(electronic bk.) |
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| 020 |
_a1118555287 _q(electronic bk.) |
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| 020 |
_a9781118555552 _q(electronic bk.) |
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| 020 |
_a1118555554 _q(electronic bk.) |
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| 035 | _a(OCoLC)827207941 | ||
| 050 | 4 | _aQA276 .G384 2012 | |
| 072 | 7 |
_aMAT _x029000 _2bisacsh |
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| 082 | 0 | 4 | _a519.50285 |
| 049 | _aMAIN | ||
| 100 | 1 | _aGivens, Geof H. | |
| 245 | 1 | 0 | _aComputational Statistics. |
| 250 | _a2nd ed. | ||
| 260 |
_aChicester : _bWiley, _c2012. |
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| 300 | _a1 online resource (491 pages). | ||
| 336 |
_atext _btxt _2rdacontent |
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| 337 |
_acomputer _bc _2rdamedia |
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| 338 |
_aonline resource _bcr _2rdacarrier |
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| 490 | 1 | _aWiley Series in Computational Statistics | |
| 505 | 0 | _aCOMPUTATIONAL STATISTICS; CONTENTS; PREFACE; ACKNOWLEDGMENTS; 1 REVIEW; 1.1 Mathematical Notation; 1.2 Taylor's Theorem and Mathematical Limit Theory; 1.3 Statistical Notation and Probability Distributions; 1.4 Likelihood Inference; 1.5 Bayesian Inference; 1.6 Statistical Limit Theory; 1.7 Markov Chains; 1.8 Computing; PART I: OPTIMIZATION; 2 OPTIMIZATION AND SOLVING NONLINEAR EQUATIONS; 2.1 Univariate Problems; 2.1.1 Newton's Method; 2.1.1.1 Convergence Order; 2.1.2 Fisher Scoring; 2.1.3 Secant Method; 2.1.4 Fixed-Point Iteration; 2.1.4.1 Scaling; 2.2 Multivariate Problems. | |
| 505 | 8 | _a2.2.1 Newton's Method and Fisher Scoring; 2.2.1.1 Iteratively Reweighted Least Squares; 2.2.2 Newton-Like Methods; 2.2.2.1 Ascent Algorithms; 2.2.2.2 Discrete Newton and Fixed-Point Methods; 2.2.2.3 Quasi-Newton Methods; 2.2.3 Gauss-Newton Method; 2.2.4 Nelder-Mead Algorithm; 2.2.5 Nonlinear Gauss-Seidel Iteration; Problems; 3 COMBINATORIAL OPTIMIZATION; 3.1 Hard Problems and NP-Completeness; 3.1.1 Examples; 3.1.2 Need for Heuristics; 3.2 Local Search; 3.3 Simulated Annealing; 3.3.1 Practical Issues; 3.3.1.1 Neighborhoods and Proposals; 3.3.1.2 Cooling Schedule and Convergence. | |
| 505 | 8 | _a3.3.2 Enhancements; 3.4 Genetic Algorithms; 3.4.1 Definitions and the Canonical Algorithm; 3.4.1.1 Basic Definitions; 3.4.1.2 Selection Mechanisms and Genetic Operators; 3.4.1.3 Allele Alphabets and Genotypic Representation; 3.4.1.4 Initialization, Termination, and Parameter Values; 3.4.2 Variations; 3.4.2.1 Fitness; 3.4.2.2 Selection Mechanisms and Updating Generations; 3.4.2.3 Genetic Operators and Permutation Chromosomes; 3.4.3 Initialization and Parameter Values; 3.4.4 Convergence; 3.5 Tabu Algorithms; 3.5.1 Basic Definitions; 3.5.2 The Tabu List; 3.5.3 Aspiration Criteria. | |
| 505 | 8 | _a3.5.4 Diversification; 3.5.5 Intensification; 3.5.6 Comprehensive Tabu Algorithm; Problems; 4 EM OPTIMIZATION METHODS; 4.1 Missing Data, Marginalization, and Notation; 4.2 The EM Algorithm; 4.2.1 Convergence; 4.2.2 Usage in Exponential Families; 4.2.3 Variance Estimation; 4.2.3.1 Louis's Method; 4.2.3.2 SEM Algorithm; 4.2.3.3 Bootstrapping; 4.2.3.4 Empirical Information; 4.2.3.5 Numerical Differentiation; 4.3 EM Variants; 4.3.1 Improving the E Step; 4.3.1.1 Monte Carlo EM; 4.3.2 Improving the M Step; 4.3.2.1 ECM Algorithm; 4.3.2.2 EM Gradient Algorithm; 4.3.3 Acceleration Methods. | |
| 505 | 8 | _a4.3.3.1 Aitken Acceleration; 4.3.3.2 Quasi-Newton Acceleration; Problems; PART II: INTEGRATION AND SIMULATION; 5 NUMERICAL INTEGRATION; 5.1 Newton-Côtes Quadrature; 5.1.1 Riemann Rule; 5.1.2 Trapezoidal Rule; 5.1.3 Simpson's Rule; 5.1.4 General kth-Degree Rule; 5.2 Romberg Integration; 5.3 Gaussian Quadrature; 5.3.1 Orthogonal Polynomials; 5.3.2 The Gaussian Quadrature Rule; 5.4 Frequently Encountered Problems; 5.4.1 Range of Integration; 5.4.2 Integrands with Singularities or Other Extreme Behavior; 5.4.3 Multiple Integrals; 5.4.4 Adaptive Quadrature; 5.4.5 Software for Exact Integration Problems. | |
| 520 | _aThis new edition continues to serve as a comprehensive guide to modern and classical methods of statistical computing. The book is comprised of four main parts spanning the field: Optimization, Integration and Simulation, Bootstrapping, Density Estimation and Smoothing. Within these sections, each chapter includes a comprehensive introduction and step-by-step implementation summaries to accompany the explanations of key methods. The new edition includes updated coverage and existing topics as well as new topics. | ||
| 588 | 0 | _aPrint version record. | |
| 650 | 0 |
_aMathematical statistics _xData processing. |
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| 650 | 4 | _aComputational statistics. | |
| 650 | 4 |
_aProbabilities _xData processing. |
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| 650 | 4 |
_aStatistics _xData processing. |
|
| 650 | 7 |
_aMATHEMATICS _xProbability & Statistics _xGeneral. _2bisacsh |
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| 650 | 7 |
_aMathematical statistics _xData processing. _2fast _0(OCoLC)fst01012133 |
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| 655 | 4 | _aElectronic books. | |
| 700 | 1 |
_aHoeting, Jennifer A. _q(Jennifer Ann), _d1966- |
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| 776 | 0 | 8 |
_iPrint version: _aGivens, Geof H. _tComputational Statistics. _dChicester : Wiley, ©2012 _z9780470533314 |
| 830 | 0 | _aWiley series in computational statistics. | |
| 856 | 4 | 0 |
_uhttp://onlinelibrary.wiley.com/book/10.1002/9781118555552 _zWiley Online Library |
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