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Springer Particle Filters for Random Set Models

Name: Particle Filters for Random Set Models
Brand: Springer

AI & Machine Learning

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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.

Product Specifications

Brand: Springer
Format: hardcover
ASIN: 1461463157
Category: Books > Subjects > Computing & Internet > Computer Science > AI & Machine Learning
Domain: Amazon UK
Release Date: 15 April 2013
Listed Since: 16 November 2012

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Springer Particle Filters for Random Set Models

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