Particle Filters for Random Set Models (Hardcover, 2013 ed.)

This book discusses state estimation of stochastic dynamic systems from noisy measurements, specifically sequential Bayesian estimation and nonlinear or stochastic filtering. The class of solutions presented in this book is based on the Monte Carlo statistical method. Although the resulting algorithms, known as particle filters, have been around for more than a decade, the recent theoretical developments of sequential Bayesian estimation in the framework of random set theory have provided new opportunities which are not widely known and are covered in this book. This book is ideal for graduate students, researchers, scientists and engineers interested in Bayesian estimation.

General

Imprint:	Springer-Verlag New York
Country of origin:	United States
Release date:	May 2013
First published:	2013
Authors:	Branko Ristic
Dimensions:	235 x 155 x 18mm (L x W x T)
Format:	Hardcover
Pages:	174
Edition:	2013 ed.
ISBN-13:	978-1-4614-6315-3
Categories:	Books > Computing & IT > General theory of computing > Mathematical theory of computation Books > Science & Mathematics > Mathematics > Applied mathematics > Mathematics for scientists & engineers
LSN:	1-4614-6315-7
Barcode:	9781461463153

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