# Fundamentals of Stochastic Filtering [electronic resource] / by Alan Bain, Dan Crisan.

Material type: TextSeries: Stochastic Modelling and Applied Probability ; 60Publisher: New York, NY : Springer New York : Imprint: Springer, 2009Description: XIII, 390 p. online resourceContent type: text Media type: computer Carrier type: online resourceISBN: 9780387768960Subject(s): Mathematics | Economics, Mathematical | Numerical analysis | Probabilities | Control engineering | Robotics | Mechatronics | Mathematics | Probability Theory and Stochastic Processes | Control, Robotics, Mechatronics | Numerical Analysis | Quantitative FinanceAdditional physical formats: Printed edition:: No titleDDC classification: 519.2 LOC classification: QA273.A1-274.9QA274-274.9Online resources: Click here to access onlineItem type | Current library | Collection | Call number | Status | Date due | Barcode | Item holds |
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Filtering Theory -- The Stochastic Process ? -- The Filtering Equations -- Uniqueness of the Solution to the Zakai and the Kushner–Stratonovich Equations -- The Robust Representation Formula -- Finite-Dimensional Filters -- The Density of the Conditional Distribution of the Signal -- Numerical Algorithms -- Numerical Methods for Solving the Filtering Problem -- A Continuous Time Particle Filter -- Particle Filters in Discrete Time.

The objective of stochastic filtering is to determine the best estimate for the state of a stochastic dynamical system from partial observations. The solution of this problem in the linear case is the well known Kalman-Bucy filter which has found widespread practical application. The purpose of this book is to provide a rigorous mathematical treatment of the non-linear stochastic filtering problem using modern methods. Particular emphasis is placed on the theoretical analysis of numerical methods for the solution of the filtering problem via particle methods. The book should provide sufficient background to enable study of the recent literature. While no prior knowledge of stochastic filtering is required, readers are assumed to be familiar with measure theory, probability theory and the basics of stochastic processes. Most of the technical results that are required are stated and proved in the appendices. The book is intended as a reference for graduate students and researchers interested in the field. It is also suitable for use as a text for a graduate level course on stochastic filtering. Suitable exercises and solutions are included.