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  • Open Daily: 10am - 10pm
    Alley-side Pickup: 10am - 7pm

    3038 Hennepin Ave Minneapolis, MN
    612-822-4611

Open Daily: 10am - 10pm | Alley-side Pickup: 10am - 7pm
3038 Hennepin Ave Minneapolis, MN
612-822-4611
Particle Filters for Random Set Models

Particle Filters for Random Set Models

Ristic, Branko

Paperback

Technology & EngineeringGeneral ComputersGeneral Mathematics

ISBN10: 148998884X
ISBN13: 9781489988843
Publisher: Springer Nature
Published: May 22 2015
Pages: 174
Weight: 0.60
Height: 0.40 Width: 6.14 Depth: 9.21
Language: English
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.

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