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Published by Morgan & Claypool Publishers, 2013
ISBN 10: 1627051198ISBN 13: 9781627051194
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Published by Springer International Publishing Jan 2013, 2013
ISBN 10: 3031014073ISBN 13: 9783031014079
Seller: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germany
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Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -In these notes, we introduce particle filtering as a recursive importance sampling method that approximates the minimum-mean-square-error (MMSE) estimate of a sequence of hidden state vectors in scenarios where the joint probability distribution of the states and the observations is non-Gaussian and, therefore, closed-form analytical expressions for the MMSE estimate are generally unavailable.We begin the notes with a review of Bayesian approaches to static (i.e., time-invariant) parameter estimation. In the sequel, we describe the solution to the problem of sequential state estimation in linear, Gaussian dynamic models, which corresponds to the well-known Kalman (or Kalman-Bucy) filter. Finally, we move to the general nonlinear, non-Gaussian stochastic filtering problem and present particle filtering as a sequential Monte Carlo approach to solve that problem in a statistically optimal way.We review several techniques to improve the performance of particle filters, including importance function optimization, particle resampling, Markov Chain Monte Carlo move steps, auxiliary particle filtering, and regularized particle filtering. We also discuss Rao-Blackwellized particle filtering as a technique that is particularly well-suited for many relevant applications such as fault detection and inertial navigation. Finally, we conclude the notes with a discussion on the emerging topic of distributed particle filtering using multiple processors located at remote nodes in a sensor network.Throughout the notes, we often assume a more general framework than in most introductory textbooks by allowing either the observation model or the hidden state dynamic model to include unknown parameters. In a fully Bayesian fashion, we treat those unknown parameters also as random variables. Using suitable dynamic conjugate priors, that approach can be applied then to perform joint state and parameter estimation.Table of Contents: Introduction / Bayesian Estimation of Static Vectors / The Stochastic Filtering Problem / Sequential Monte Carlo Methods / Sampling/Importance Resampling (SIR) Filter / Importance Function Selection / Markov Chain Monte Carlo Move Step / Rao-Blackwellized Particle Filters / Auxiliary Particle Filter / Regularized Particle Filters / Cooperative Filtering with Multiple Observers / Application Examples / Summary 100 pp. Englisch.
Published by Morgan & Claypool Publishers, 2013
ISBN 10: 1627051198ISBN 13: 9781627051194
Seller: GF Books, Inc., Hawthorne, CA, U.S.A.
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Published by Springer International Publishing, 2013
ISBN 10: 3031014073ISBN 13: 9783031014079
Seller: AHA-BUCH GmbH, Einbeck, Germany
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Taschenbuch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - In these notes, we introduce particle filtering as a recursive importance sampling method that approximates the minimum-mean-square-error (MMSE) estimate of a sequence of hidden state vectors in scenarios where the joint probability distribution of the states and the observations is non-Gaussian and, therefore, closed-form analytical expressions for the MMSE estimate are generally unavailable.We begin the notes with a review of Bayesian approaches to static (i.e., time-invariant) parameter estimation. In the sequel, we describe the solution to the problem of sequential state estimation in linear, Gaussian dynamic models, which corresponds to the well-known Kalman (or Kalman-Bucy) filter. Finally, we move to the general nonlinear, non-Gaussian stochastic filtering problem and present particle filtering as a sequential Monte Carlo approach to solve that problem in a statistically optimal way.We review several techniques to improve the performance of particle filters, including importance function optimization, particle resampling, Markov Chain Monte Carlo move steps, auxiliary particle filtering, and regularized particle filtering. We also discuss Rao-Blackwellized particle filtering as a technique that is particularly well-suited for many relevant applications such as fault detection and inertial navigation. Finally, we conclude the notes with a discussion on the emerging topic of distributed particle filtering using multiple processors located at remote nodes in a sensor network.Throughout the notes, we often assume a more general framework than in most introductory textbooks by allowing either the observation model or the hidden state dynamic model to include unknown parameters. In a fully Bayesian fashion, we treat those unknown parameters also as random variables. Using suitable dynamic conjugate priors, that approach can be applied then to perform joint state and parameter estimation.Table of Contents: Introduction / Bayesian Estimation of Static Vectors / The Stochastic Filtering Problem / Sequential Monte Carlo Methods / Sampling/Importance Resampling (SIR) Filter / Importance Function Selection / Markov Chain Monte Carlo Move Step / Rao-Blackwellized Particle Filters / Auxiliary Particle Filter / Regularized Particle Filters / Cooperative Filtering with Multiple Observers / Application Examples / Summary.
Published by Springer, 2001
ISBN 10: 0387951466ISBN 13: 9780387951461
Seller: HPB-Red, Dallas, TX, U.S.A.
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Hardcover. Condition: Good. Connecting readers with great books since 1972! Used textbooks may not include companion materials such as access codes, etc. May have some wear or writing/highlighting. We ship orders daily and Customer Service is our top priority!.
Published by Springer, 2001
ISBN 10: 0387951466ISBN 13: 9780387951461
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Published by Springer, 2001
ISBN 10: 0387951466ISBN 13: 9780387951461
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Published by Springer, 2001
ISBN 10: 0387951466ISBN 13: 9780387951461
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Published by Springer, 2001
ISBN 10: 0387951466ISBN 13: 9780387951461
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Published by Springer, Berlin|Springer International Publishing|Morgan & Claypool|Springer, 2013
ISBN 10: 3031014073ISBN 13: 9783031014079
Seller: moluna, Greven, Germany
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Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. In these notes, we introduce particle filtering as a recursive importance sampling method that approximates the minimum-mean-square-error (MMSE) estimate of a sequence of hidden state vectors in scenarios where the joint probability distribution of the stat.
Published by Wiley, 2013
ISBN 10: 1118612264ISBN 13: 9781118612262
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Published by John Wiley & Sons, 2013
ISBN 10: 1118612264ISBN 13: 9781118612262
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Condition: New. pp. 216.
Published by Wiley, 2013
ISBN 10: 1118612264ISBN 13: 9781118612262
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Published by John Wiley & Sons, 2013
ISBN 10: 1118612264ISBN 13: 9781118612262
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Published by Wiley, 2013
ISBN 10: 1118612264ISBN 13: 9781118612262
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Published by Wiley, 2013
ISBN 10: 1118612264ISBN 13: 9781118612262
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Published by Wiley 2014-01-28, Hoboken, New Jersey, 2014
ISBN 10: 1118612264ISBN 13: 9781118612262
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Published by Wiley, 2013
ISBN 10: 1118612264ISBN 13: 9781118612262
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ISBN 10: 1118612264ISBN 13: 9781118612262
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Published by John Wiley & Sons Inc, 2013
ISBN 10: 1118612264ISBN 13: 9781118612262
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Published by John Wiley and Sons, 2013
ISBN 10: 1118612264ISBN 13: 9781118612262
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Published by Wiley-Blackwell 2014-01-28, 2014
ISBN 10: 1118612264ISBN 13: 9781118612262
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Published by Springer, 2001
ISBN 10: 0387951466ISBN 13: 9780387951461
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Published by Springer, 2001
ISBN 10: 0387951466ISBN 13: 9780387951461
Seller: Books Unplugged, Amherst, NY, U.S.A.
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Published by John Wiley & Sons Inc, 2013
ISBN 10: 1118612264ISBN 13: 9781118612262
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Published by John Wiley & Sons, 2014
ISBN 10: 1118612264ISBN 13: 9781118612262
Seller: moluna, Greven, Germany
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Condition: New. REUVEN Y. RUBINSTEIN, DSc, was Professor Emeritus in the Faculty of Industrial Engineering and Management at Technion-Israel Institute of Technology. The author of over 100 articles and six books, Dr. Rubinstein was also the inventor of the popular score-fu.
Published by John Wiley & Sons Inc, 2014
ISBN 10: 1118612264ISBN 13: 9781118612262
Seller: Kennys Bookshop and Art Galleries Ltd., Galway, GY, Ireland
Book First Edition
Condition: New. This book presents the first comprehensive account of fast sequential Monte Carlo (SMC) methods for counting and optimization at an exceptionally accessible level. Written by authorities in the field, it places great emphasis on cross-entropy, minimum cross-entropy, splitting, and stochastic enumeration. Series: Wiley Series in Probability and Statistics. Num Pages: 208 pages, Illustrations. BIC Classification: PBKS; PBT. Category: (P) Professional & Vocational. Dimension: 167 x 242 x 16. Weight in Grams: 426. . 2013. 1st Edition. Hardcover. . . . .
Published by John Wiley & Sons Inc, 2013
ISBN 10: 1118612264ISBN 13: 9781118612262
Seller: Revaluation Books, Exeter, United Kingdom
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Hardcover. Condition: Brand New. 1st edition. 208 pages. 9.00x6.00x0.75 inches. In Stock.
Published by Springer, 2010
ISBN 10: 1441928871ISBN 13: 9781441928870
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Published by Springer, 2024
ISBN 10: 0387951466ISBN 13: 9780387951461
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