Sequential Monte Carlo Methods in Practice / Edition 1

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Monte Carlo methods are revolutionizing 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 survival of the fittest, have made it possible to solve numerically many complex, nonstandard problems that were previously intractable.

This book presents the first comprehensive and coherent treatment of these techniques, including convergence results and applications to tracking, guidance, automated target recognition, aircraft navigation, robot navigation, econometrics, financial modelling, neural networks, optimal control, optimal filtering, communications, reinforcement learning, signal enhancement, model averaging and selection, computer vision, semiconductor design, population biology, dynamic Bayesian networks, and time series analysis. This book will be of great value to students, researchers, and practitioners who have some basic knowledge of probability.

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Editorial Reviews

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From the reviews:


"…a remarkable, successful effort at making these ideas available to statisticians. It gives an overview, presents available theory, gives a splendid development of various bells and whistles important in practical implementation, and finally gives a large number of detailed examples and case studies…The authors and editors have been careful to write in a unified, readable way…I find it remarkable that the editors and authors have combined to produce an accessible bible that will be studied and used for years to come."

"Usually, very few volumes edited from papers contributed by many different authors result in books which can serve as either good textbooks or as useful reference. However, in the case of this book, it is enough to read the foreword by Adrian Smith to realize that this particular volume is quite different. … it is a good reference book for SMC." (Mohan Delampady, Sankhya: Indian Journal of Statistics, Vol. 64 (A), 2002)

"In this book the authors present sequential Monte Carlo (SMC) methods … . Over the last few years several closely related algorithms have appeared under the names ‘boostrap filters’, ‘particle filters’, ‘Monte Carlo filters’, and ‘survival of the fittest’. The book under review brings together many of these algorithms and presents theoretical developments … . This book will be of great value to advanced students, researchers, and practitioners who want to learn about sequential Monte Carlo methods for the computational problems of Bayesian Statistics." (E. Novak, Metrika, May, 2003)

"This book provides a very good overview of the sequential Monte Carlo methods and contains many ideas on further research on methodologies and newer areas of application. … It will be certainly a valuable reference book for students and researchers working in the area of on-line data analysis. … the techniques discussed in this book are of great relevance to practitioners dealing with real time data." (Pradipta Sarkar, Technometrics, Vol. 45 (1), 2003)

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Product Details

  • ISBN-13: 9781441928870
  • Publisher: Springer New York
  • Publication date: 12/1/2010
  • Series: Information Science and Statistics Series
  • Edition description: Softcover reprint of hardcover 1st ed. 2001
  • Edition number: 1
  • Pages: 582
  • Product dimensions: 1.24 (w) x 9.21 (h) x 6.14 (d)

Table of Contents

Tutorial Chapter
• Particle Filters - A Theoretical Perspective
• Interacting Particle System Approximation Methods for Feynman-Kac Formulae and Nonlinear Filtering
• Interacting Parallel Chains for Sequential Bayesian Estimation
• Stochastic and Deterministic Particle Filters
• Super-Efficient Particle Filters for Tracking Problems
• Following a Moving Target - Monte Carlo Inference for Dynamic Bayesian Models
• Improvement Strategies for Particle Filters with Examples from Communications and Audio Signal Processing
• Approximating and Maximizing the Likelihood for a General State Space Model
• Analysis and Implementation Issues of Regularized Particle Filters
• Combined Parameter and State Estimation in Simulation-based Filtering
• Sequential Importance Sampling
• Auxiliary Variable Based Particle Filters
• Improved Particle Filters and Smoothing
• Terrain Navigation Using Sequential Monte Carlo Methods
• Statistical Models of Visual Shape and Motion
• Sequential Monte Carlo Methods for Neural Networks
• Short Term Forecasting of Electricity Load
• Particles and Mixtures for Tracking and Guidance
• Monte Carlo Filter Approach to an Analysis of Small Count Time Series
• Monte Carlo Smoothing and Self-Organizing State Space Model
• Sequential Monte Carlo Methods Applied to Graphical Models
• In-situ Ellipsometry
• Maneuvering Target Tracking Using a Multiple Model Bootstrap Filter
• Particle Filters and Diagnostic Checking in Time Series
• MCMC Estimation on Transformation Groups for Object Recognition
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