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Sequential Monte Carlo Methods in Practice

Monte Carlo methods are revolutionising the on-line analysis of data in fields as diverse as financial modelling, target tracking and computer vision. These methods, appearing under the names of bootstrap filters, condensation, optimal Monte Carlo filters, particle filters and survial of the fittest...

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Bibliographic Details
Other Authors: Doucet, Arnaud (Editor), Freitas, Nando de (Editor), Gordon, Neil (Editor)
Format: eBook
Language:English
Published: New York, NY Springer New York 2001, 2001
Edition:1st ed. 2001
Series:Information Science and Statistics
Subjects:
Statistical Theory And Methods
Statistics 
Statistics In Engineering, Physics, Computer Science, Chemistry And Earth Sciences
Online Access:
Collection: Springer Book Archives -2004 - Collection details see MPG.ReNa
Table of Contents:
  • 1 An Introduction to Sequential Monte Carlo Methods
  • 2 Particle Filters — A Theoretical Perspective
  • 3 Interacting Particle Filtering With Discrete Observations
  • 4 Sequential Monte Carlo Methods for Optimal Filtering
  • 5 Deterministic and Stochastic Particle Filters in State-Space Models
  • 6 RESAMPLE—MOVE Filtering with Cross-Model Jumps
  • 7 Improvement Strategies for Monte Carlo Particle Filters
  • 8 Approximating and Maximising the Likelihood for a General State-Space Model
  • 9 Monte Carlo Smoothing and Self-Organising State-Space Model
  • 10 Combined Parameter and State Estimation in Simulation-Based Filtering
  • 11 A Theoretical Framework for Sequential Importance Sampling with Resampling
  • 12 Improving Regularised Particle Filters
  • 13 Auxiliary Variable Based Particle Filters
  • 14 Improved Particle Filters and Smoothing
  • 15 Posterior Cramér-Rao Bounds for Sequential Estimation
  • 16 Statistical Models of Visual Shape and Motion
  • 17 Sequential Monte Carlo Methods for Neural Networks
  • 18 Sequential Estimation of Signals under Model Uncertainty
  • 19 Particle Filters for Mobile Robot Localization
  • 20 Self-Organizing Time Series Model
  • 21 Sampling in Factored Dynamic Systems
  • 22 In-Situ Ellipsometry Solutions Using Sequential Monte Carlo
  • 23 Manoeuvring Target Tracking Using a Multiple-Model Bootstrap Filter
  • 24 Rao-Blackwellised Particle Filtering for Dynamic Bayesian Networks
  • 25 Particles and Mixtures for Tracking and Guidance
  • 26 Monte Carlo Techniques for Automated Target Recognition

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