The Oxford Handbook of Nonlinear Filtering (Hardcover)

From the contents: 1: D. Crisan, B. Rozovsky: Introduction; 2: The Foundations of Nonlinear Filtering; 2.1: H. Kunita: Nonlinear Filtering Problems I. Bayes Formulae and Innovations; 2.2: H. Kunita: Nonlinear Filtering Problems II. Associated Equations; 2.3: B. Grigelionis & R. Mikulevicius: Nonlinear Filtering Equations for Processes With Jumps; 2.4: T. G. Kurtz & G. Nappo: The Filtered Martingale Problem; 3: Nonlinear Filtering and Stochastic Partial Differential Equations; 3.1: N. V. Krylov: Filtering Equations for Partially Observable Diffusion Processes With Lipschitz Continuous Coefficients; 3.2: M. Chaleyat-Maurel: Malliavin Calculus Applications to the Study of Nonlinear Filtering; 3.3: S. V. Lototsky: Chaos Expansion to Nonlinear Filtering; 4: Stability and Asymptotic Analysis; 4.1: M. L. Kleptsyna & A. Y. Veretennikov: On Filtering with Unspecified Initial Data for Non-uniformly Ergodic Signals; 4.2: R. Atar: Exponential Decay Rate of the Filter's Dependence on the Initial Distribution; 4.3: P. Chigansky, R. Liptser & R. Van Handel: Intrinsic Methods in Filter Stability; 4.4: A. Budhiraja: Feller and Stability Properties of the Nonlinear Filter; 4.5: W. Stannat: Lipschitz Continuity of Feynman-Kac Propagators; 5: Special Topics; 5.1: M. Davis: Pathwise Nonlinear Filtering; 5.2: A. J. Heunis: The Innovation Problem; 5.3: T. Duncan: Nonlinear Filtering and Fractional Brownian Motion; 6: Estimation and Control; 6.1: N. J. Newton: Dual Filters, Path Estimators and Information; 6.2: A. Bensoussan, M. Cakanyildirim & S. P. Sethi: Filtering for Discrete-Time Markov Processes and Applications to Inventory Control with Incomplete Information; 6.3: H. A.P. Blom & Y. Bar-Shalom: Bayesian Filtering of Stochastic Hybrid Systems in Discrete-time and Interacting Multiple Model; 7: Approximation Theory; 7.1: O. Zeitouni: Error Bounds for the Nonlinear Filtering of Diffusion Processes; 7.2: D. Crisan: Discretizing the Continuous Time Filtering Problem. Order of Convergence; 7.3: F. Le Gland, V. Monbet & V.-D. Tran: Large Sample Asymptotics for the Ensemble Kalman Filter; 8: The Particle Approach; 8.1: J. Xiong: Particle Approximations to the Filtering Problem in Continuous Time; 8.2: A. Doucet & A. M. Johansen: Tutorial on Particle Filtering and Smoothing: Fifteen Years Later; 8.3: P. Del Moral, F. Patras & S. Rubenthaler: A Mean Field Theory of Nonlinear Filtering; 8.4: T. B. Schon, F. Gustafsson & R. Karlsson: The Particle Filter in Practice; 8.5: C. Litterer & T. Lyons: Introducing Cubature to Filtering; 9: Numerical Methods in Nonlinear Filtering; 9.1: H. J. Kushner: Numerical Approximations to Optimal Nonlinear Filters; 9.2: M. Hairer, A. Stuart & J. Voss: Signal Processing Problems on Function Space: Bayesian Formulation, SPDEs and Effective MCMC Methods; 9.3: J. M. C. Clark & R. B. Vinter: Robust, Computationally Efficient Algorithms for Tracking Problems with Measurement Process Nonlinearities; 9.4: G.N. Milstein & M. Tretyakov: Nonlinear Filtering Algorithms Based on Averaging Over Characteristics and on the Innovation Approach; 10: Nonlinear Filtering in Financial Mathematics; 10.1: R. Frey & W. Runggaldier: Nonlinear Filtering in Models for Interest-Rate and Credit Risk; 10.2: R. J. Elliott, H. Miao & Z. Wu: An Asset Pricing Model with Mean Reversion and Regime Switching Stochastic Volatility; 10.3: H. Pham: Portfolio Optimization Under Partial Observation: Theoretical and Numerical Aspects; 10.4: L. C. Scott & Y. Zeng: Filtering with Counting Process Observations: Application to the Statistical Analysis of the Micromovement of Asset Price