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Bog, 2003
Introduction to stochastic search and optimization : estimation, simulation, and control (engelsk)
Detaljer
Indhold
Seneste udgave, bogPreface. Stochastic Search and Optimization: Motivation and Supporting Results. Direct Methods for Stochastic Search. Recursive Estimation for Linear Models. Stochastic Approximation for Nonlinear Root-Finding. Stochastic Gradient Form of Stochastic Approximation. Stochastic Approximation and the Finite-Difference Method. Simultaneous Perturbation Stochastic Approximation. Annealing-Type Algorithms. Evolutionary Computation I: Genetic Algorithms. Evolutionary Computation II: General Methods and Theory. Reinforcement Learning via Temporal Differences. Statistical Methods for Optimization in Discrete Problems. Model Selection and Statistical Information. Simulation-Based Optimization I: Regeneration, Common Random Numbers, and Selection Methods. Simulation-Based Optimization II: Stochastic Gradient and Sample Path Methods. Markov Chain Monte Carlo. Optimal Design for Experimental Inputs. Appendix A. Selected Results from Multivariate Analysis. Appendix B. Some Basic Tests in Statistics. Appendix C. Probability Theory and Convergence. Appendix D. Random Number Generation. Appendix E. Markov Processes. Answers to Selected Exercises. References. Frequently Used Notation. Index ; Stochastic search and optimization : motivation and supporting results -- Direct methods for stochastic search -- Recursive estimation for linear models -- Stochastic approximation for nonlinear root-finding -- Stochastic gradient form of stochastic approximation -- Stochastic approximation and the finite-difference method -- Simultaneous perturbation stochastic approximation -- Annealing-type algorithms -- Evolutionary computation I : genetic algorithms -- Evolutionary computation II : general methods and theory -- Reinforcement learning via temporal differences -- Statistical methods for optimization in discrete problems -- Model selection and statistical information -- Simulation-based optimization I : regeneration, common random numbers, and selection methods -- Simulation-based optimization II : stochastic gradient and sample path methods -- Markov chain Monte Carlo -- Optimal design for experimental inputs
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