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The Resource Sequential Monte Carlo methods in practice, Arnaud Doucet, Nando de Freitas, Neil Gordon, editors ; foreword by Adrian Smith

Sequential Monte Carlo methods in practice, Arnaud Doucet, Nando de Freitas, Neil Gordon, editors ; foreword by Adrian Smith

Label
Sequential Monte Carlo methods in practice
Title
Sequential Monte Carlo methods in practice
Statement of responsibility
Arnaud Doucet, Nando de Freitas, Neil Gordon, editors ; foreword by Adrian Smith
Contributor
Subject
Language
eng
Member of
Cataloging source
DLC
Illustrations
illustrations
Index
index present
LC call number
QA298
LC item number
.S47 2001
Literary form
non fiction
Nature of contents
bibliography
Series statement
Statistics for engineering and information science
Sequential Monte Carlo methods in practice, Arnaud Doucet, Nando de Freitas, Neil Gordon, editors ; foreword by Adrian Smith
Label
Sequential Monte Carlo methods in practice, Arnaud Doucet, Nando de Freitas, Neil Gordon, editors ; foreword by Adrian Smith
Publication
Related Contributor
Related Location
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Bibliography note
Includes bibliographical references (p. [553]-576) and index
Contents
  • III: Strategies for improving sequential Monte Carlo methods.
  • Sequential Monte Carlo methods for optimal filtering
  • Christophe Andrieu, Arnaud Doucet, and Elena Punskaya
  • Deterministic and stochastic particle filters in state-space models
  • Erik Bolviken and Geir Storvik
  • RESAMPLE-MOVE filtering with cross-model jumps
  • Carlo Berzuini and Walter Gilks
  • Improvement strategies for Monte Carlo particle filters
  • Simon Godsill and Tim Clapp
  • Approximating and maximising the likelihood for a general state-space model
  • I: Introduction.
  • Markus Hurzeler and Hans R. Kunsch
  • Monte Carlo smoothing and self-organising state-space model
  • Genshiro Kitagawa and Seisho Sato
  • Combined parameter and state estimation in simulation-based filtering
  • Jane Liu and Mike West
  • A theoretical framework for sequential importance sampling with reasampling
  • Jun S. Liu, Rong Chen, and Tanya Logvinenko
  • Improving regularised particle filters
  • Christian Musso, Nadia Oudjane, and Francois Le Gland
  • Auxiliary variable based particle filters
  • An introduction to sequential Monte Carlo methods
  • Michael K. Pitt and Neil Shephard
  • Improved particle filters and smoothing
  • Photis Stavropoulos and D.M. Titterington
  • IV: Applications.
  • Posterior Cramer-Rao bounds for sequential estimation
  • Niclas Bergman
  • Statistical models of visual shape and motion
  • Andrew Blake, Michael Isard, and John MacCormick
  • Sequential Monte Carlo methods for neural networks
  • N de Freitas...[et al.]
  • Arnaud Doucet, Nando de Freitas, and Deil Gordon
  • Sequential estimation of signals under model uncertainty
  • Petar M. Djuric
  • Particle filters for mobile robot localization
  • Dieter Fox...[et al.]
  • Self-organizing time series model
  • Tomoyuki Higuchi
  • Sampling in factored dynamic systems
  • Daphne Koller and Uri Lerner
  • In-situ ellipsometry solutions using sequential Monte Carlo
  • Alan D. Marrs
  • II: Theoretical issues.
  • Manoeuvring target tracking using a multiple-model bootstrap filter
  • Shaun McGinnity and George W. Irwin
  • Rao-Blackwellised particle filtering for dynamic Bayesian networks
  • Kevin Murphy and Stuart Russell
  • Particles and mixtures for tracking and guidance
  • David Salmond and Neil Gordon
  • Monte Carlo techniques for automated target recognition
  • Anuj Srivastava...[et al.]
  • Particle filters-a theoretical perspective
  • Dan Crisan
  • Interacting particle filtering with discrete observations
  • Pierre Del Moral and Jean Jacod /
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https://contentcafe2.btol.com/ContentCafe/Jacket.aspx?Return=1&Type=S&Value=9780387951461&userID=ebsco-test&password=ebsco-test
Dimensions
24 cm.
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{'f': 'http://opac.lib.rpi.edu/record=b1432383'}
Extent
xxvii, 581 p.
Isbn
9780387951461
Isbn Type
(alk. paper)
Lccn
00047093
Other physical details
ill.

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