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05592nam a2200637 4500 |
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ocn827207941 |
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OCoLC |
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20170124071029.9 |
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m o d |
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cr cnu---unuuu |
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130211s2012 enk o 000 0 eng d |
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|b eng
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|c EBLCP
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|d OCLCQ
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|d DG1
|d OHI
|d OCLCF
|d UKDOC
|d OCLCQ
|d DEBSZ
|d OCLCQ
|d DEBBG
|d GrThAP
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|a 9781118555286
|q (electronic bk.)
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|a 1118555287
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|a 9781118555552
|q (electronic bk.)
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|a 1118555554
|q (electronic bk.)
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|a AU@
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|a DEBBG
|b BV043395376
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|a (OCoLC)827207941
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|a QA276 .G384 2012
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|a MAT
|x 029000
|2 bisacsh
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|a 519.50285
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|a MAIN
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| 100 |
1 |
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|a Givens, Geof H.
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| 245 |
1 |
0 |
|a Computational Statistics.
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| 250 |
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|a 2nd ed.
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| 264 |
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1 |
|a Chicester :
|b Wiley,
|c 2012.
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| 300 |
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|a 1 online resource (491 pages).
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| 336 |
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|a text
|b txt
|2 rdacontent
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| 337 |
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|a computer
|b c
|2 rdamedia
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| 338 |
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|a online resource
|b cr
|2 rdacarrier
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| 490 |
1 |
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|a Wiley Series in Computational Statistics
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| 505 |
0 |
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|a COMPUTATIONAL 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.
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8 |
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|a 2.2.1 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.
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8 |
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|a 3.3.2 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.
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| 505 |
8 |
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|a 3.5.4 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.
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| 505 |
8 |
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|a 4.3.3.1 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.
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| 520 |
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|a This 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.
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| 588 |
0 |
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|a Print version record.
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| 650 |
|
0 |
|a Mathematical statistics
|x Data processing.
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| 650 |
|
4 |
|a Computational statistics.
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| 650 |
|
4 |
|a Probabilities
|x Data processing.
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| 650 |
|
4 |
|a Statistics
|x Data processing.
|
| 650 |
|
7 |
|a MATHEMATICS
|x Probability & Statistics
|x General.
|2 bisacsh
|
| 650 |
|
7 |
|a Mathematical statistics
|x Data processing.
|2 fast
|0 (OCoLC)fst01012133
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| 655 |
|
4 |
|a Electronic books.
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| 700 |
1 |
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|a Hoeting, Jennifer A.
|q (Jennifer Ann),
|d 1966-
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| 776 |
0 |
8 |
|i Print version:
|a Givens, Geof H.
|t Computational Statistics.
|d Chicester : Wiley, ©2012
|z 9780470533314
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| 830 |
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0 |
|a Wiley series in computational statistics.
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| 856 |
4 |
0 |
|u https://doi.org/10.1002/9781118555552
|z Full Text via HEAL-Link
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| 994 |
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|a 92
|b DG1
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