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· 분류 : 외국도서 > 과학/수학/생태 > 수학 > 확률과 통계 > 일반
· ISBN : 9780387202686
· 쪽수 : 556쪽
· 출판일 : 2004-03-30
목차
1 Introduction.- 1.1 On the Origins of Feynman-Kac and Particle Models.- 1.2 Notation and Conventions.- 1.3 Feynman-Kac Path Models.- 1.3.1 Path-Space and Marginal Models.- 1.3.2 Nonlinear Equations.- 1.4 Motivating Examples.- 1.4.1 Engineering Science.- 1.4.2 Bayesian Methodology.- 1.4.3 Particle and Statistical Physics.- 1.4.4 Biology.- 1.4.5 Applied Probability and Statistics.- 1.5 Interacting Particle Systems.- 1.5.1 Discrete Time Models.- 1.5.2 Continuous Time Models.- 1.6 Sequential Monte Carlo Methodology.- 1.7 Particle Interpretations.- 1.8 A Contents Guide for the Reader.- 2 Feynman-Kac Formulae.- 2.1 Introduction.- 2.2 An Introduction to Markov Chains.- 2.2.1 Canonical Probability Spaces.- 2.2.2 Path-Space Markov Models.- 2.2.3 Stopped Markov chains.- 2.2.4 Examples.- 2.3 Description of the Models.- 2.4 Structural Stability Properties.- 2.4.1 Path Space and Marginal Models.- 2.4.2 Change of Reference Probability Measures.- 2.4.3 Updated and Prediction Flow Models.- 2.5 Distribution Flows Models.- 2.5.1 Killing Interpretation.- 2.5.2 Interacting Process Interpretation.- 2.5.3 McKean Models.- 2.5.4 Kalman-Bucy filters.- 2.6 Feynman-Kac Models in Random Media.- 2.6.1 Quenched and Annealed Feynman-Kac Flows.- 2.6.2 Feynman-Kac Models in Distribution Space.- 2.7 Feynman-Kac Semigroups.- 2.7.1 Prediction Semigroups.- 2.7.2 Updated Semigroups.- 3 Genealogical and Interacting Particle Models.- 3.1 Introduction.- 3.2 Interacting Particle Interpretations.- 3.3 Particle models with Degenerate Potential.- 3.4 Historical and Genealogical Tree Models.- 3.4.1 Introduction.- 3.4.2 A Rigorous Approach and Related Transport Problems.- 3.4.3 Complete Genealogical Tree Models.- 3.5 Particle Approximation Measures.- 3.5.1 Some Convergence Results.- 3.5.2 Regularity Conditions.- 4 Stability of Feynman-Kac Semigroups.- 4.1 Introduction.- 4.2 Contraction Properties of Markov Kernels.- 4.2.1 h-relative Entropy.- 4.2.2 Lipschitz Contractions.- 4.3 Contraction Properties of Feynman-Kac Semigroups.- 4.3.1 Functional Entropy Inequalities.- 4.3.2 Contraction Coefficients.- 4.3.3 Strong Contraction Estimates.- 4.3.4 Weak Regularity Properties.- 4.4 Updated Feynman-Kac Models.- 4.5 A Class of Stochastic Semigroups.- 5 Invariant Measures and Related Topics.- 5.1 Introduction.- 5.2 Existence and Uniqueness.- 5.3 Invariant Measures and Feynman-Kac Modeling.- 5.4 Feynman-Kac and Metropolis-Hastings Models.- 5.5 Feynman-Kac-Metropolis Models.- 5.5.1 Introduction.- 5.5.2 The Genealogical Metropolis Particle Model.- 5.5.3 Path Space Models and Restricted Markov Chains.- 5.5.4 Stability Properties.- 6 Annealing Properties.- 6.1 Introduction.- 6.2 Feynman-Kac-Metropolis Models.- 6.2.1 Description of the Model.- 6.2.2 Regularity Properties.- 6.2.3 Asymptotic Behavior.- 6.3 Feynman-Kac Trapping Models.- 6.3.1 Description of the Model.- 6.3.2 Regularity Properties.- 6.3.3 Asymptotic Behavior.- 6.3.4 Large-Deviation Analysis.- 6.3.5 Concentration Levels.- 7 Asymptotic Behavior.- 7.1 Introduction.- 7.2 Some Preliminaries.- 7.2.1 McKean Interpretations.- 7.2.2 Vanishing Potentials.- 7.3 Inequalities for Independent Random Variables.- 7.3.1 Lp and Exponential Inequalities.- 7.3.2 Empirical Processes.- 7.4 Strong Law of Large Numbers.- 7.4.1 Extinction Probabilities.- 7.4.2 Convergence of Empirical Processes.- 7.4.3 Time-Uniform Estimates.- 8 Propagation of Chaos.- 8.1 Introduction.- 8.2 Some Preliminaries.- 8.3 Outline of Results.- 8.4 Weak Propagation of Chaos.- 8.5 Relative Entropy Estimates.- 8.6 A Combinatorial Transport Equation.- 8.7 Asymptotic Properties of Boltzmann-Gibbs Distributions.- 8.8 Feynman-Kac Semigroups.- 8.8.1 Marginal Models.- 8.8.2 Path-Space Models.- 8.9 Total Variation Estimates.- 9 Central Limit Theorems.- 9.1 Introduction.- 9.2 Some Preliminaries.- 9.3 Some Local Fluctuation Results.- 9.4 Particle Density Profiles.- 9.4.1 Unnormalized Measures.- 9.4.2 Normalized Measures.- 9.4.3 Killing Interpretations and Related Comparisons.- 9.5 A














