Statistics of Random Processes I

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Edition: 2nd
Format: Hardcover
Pub. Date: 2000-12-01
Publisher(s): Springer Nature
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Summary

The subject of these two volumes is non-linear filtering (prediction and smoothing) theory and its application to the problem of optimal estimation, control with incomplete data, information theory, and sequential testing of hypothesis. The book is not only addressed to mathematicians but should also serve the interests of other scientists who apply probabilistic and statistical methods in their work. The theory of martingales presented in the book has an independent interest in connection with problems from financial mathematics. In the second edition, the authors have made numerous corrections, updating every chapter, adding two new subsections devoted to the Kalman filter under wrong initial conditions, as well as a new chapter devoted to asymptotically optimal filtering under diffusion approximation. Moreover, in each chapter a comment is added about the progress of recent years.

Table of Contents

Preface to the Second Edition v
Introduction 1(10)
Essentials of Probability Theory and Mathematical Statistics
11(28)
Main Concepts of Probability Theory
11(9)
Random Processes: Basic Notions
20(5)
Markov Times
25(5)
Brownian Motion Processes
30(4)
Some Notions from Mathematical Statistics
34(5)
Martingales and Related Processes: Discrete Time
39(18)
Supermartingales and Submartingales on a Finite Time Interval
39(6)
Submartingales on an Infinite Time Interval, and the Theorem of Convergence
45(2)
Regular Martingales: Levy's Theorem
47(3)
Invariance of the Supermartingale Property for Markov Times: Riesz and Doob Decompositions
50(7)
Martingales and Related Processes: Continuous Time
57(28)
Right Continuous Supermartingales
57(3)
Basic Inequalities, the Theorem of Convergence, and Invariance of the Supermartingale Property for Markov Times
60(4)
Doob-Meyer Decomposition for Supermartingales
64(10)
Some Properties of Predictable Increasing Processes
74(11)
The Wiener Process, the Stochastic Integral over the Wiener Process, and Stochastic Differential Equations
85(76)
The Wiener Process as a Square Integrable Martingale
85(7)
Stochastic Integrals: Ito Processes
92(31)
Ito's Formula
123(9)
Strong and Weak Solutions of Stochastic Differential Equations
132(29)
Square Integrable Martingales and Structure of the Functionals on a Wiener Process
161(58)
Doob-Meyer Decomposition for Square Integrable Martingales
161(9)
Representation of Square Integrable Martingales
170(4)
The Structure of Functionals of a Wiener Process
174(8)
Stochastic Integrals over Square Integrable Martingales
182(11)
Integral Representations of the Martingales which are Conditional Expectations and the Fubini Theorem for Stochastic Integrals
193(7)
The Structure of Functionals of Processes of the Diffusion Type
200(19)
Nonnegative Supermartingales and Martingales, and the Girsanov Theorem
219(32)
Nonnegative Supermartingales
219(9)
Nonnegative Martingales
228(10)
The Girshanov Theorem and its Generalization
238(13)
Absolute Continuity of Measures corresponding to the Ito Processes and Processes of the Diffusion Type
251(66)
The Ito Processes, and the Absolute Continuity of their Measures with respect to Wiener Measure
251(6)
Processes of the Diffusion Type: The Absolute Continuity of their Measures with respect to Wiener Measure
257(14)
The Structure of Processes whose Measure is Absolutely Continuous with Respect to Wiener Measure
271(2)
Representation of the Ito Processes as Processes of the Diffusion Type, Innovation Processes, and the Structure of Functionals on the Ito Process
273(6)
The Case of Gaussian Processes
279(7)
The Absolute Continuity of Measures of the Ito Processes with respect to Measures Corresponding to Processes of the Diffusion Type
286(11)
The Cameron-Martin Formula
297(2)
The Cramer-Wolfowitz Inequality
299(4)
An Abstract Version of the Bayes Formula
303(14)
General Equations of Optimal Nonlinear Filtering, Interpolation and Extrapolation of Partially Observable Random Processes
317(34)
Filtering: the Main Theorem
317(2)
Filtering: Proof of the Main Theorem
319(7)
Filtering of Diffusion Markov Processes
326(3)
Equations of Optimal Nonlinear Interpolation
329(2)
Equations of Optimal Nonlinear Extrapolation
331(3)
Stochastic Differential Equations with Partial Derivatives for the Conditional Density (the Case of Diffusion Markov Processes)
334(17)
Optimal Filtering, Interpolation and Extrapolation of Markov Processes with a Countable Number of States
351(24)
Equations of Optimal Nonlinear Filtering
351(12)
Forward and Backward Equations of Optimal Nonlinear Interpolation
363(5)
Equations of Optimal Nonlinear Extrapolation
368(3)
Examples
371(4)
Optimal Linear Nonstationary Filtering
375(34)
The Kalman-Bucy Method
375(14)
Martingale Proof of the Equations of Linear Nonstationary Filtering
389(3)
Equations of Linear Nonstationary Filtering: the Multidimensional Case
392(8)
Equations for an Almost Linear Filter for Singular B º B
400(9)
Bibliography 409(16)
Index 425

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