- Monte Carlo Methods is a very hot area of research
- Book's emphasis is on applications that span many disciplines
- requires only basic knowledge of probability
Monte Carlo methods are revolutionizing the on-line analysis of data in many fileds. They have made it possible to solve numerically many complex, non-standard problems that were previously intractable. This book presents the first comprehensive treatment of these techniques.|Monte Carlo methods are revolutionizing the on-line analysis of data in fields as diverse as financial modeling, 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, non-standard problems that were previously intractable.This book presents the first comprehensive treatment of these techniques, including convergence results and applications to tracking, guidance, automated target recognition, aircraft navigation, robot navigation, econometrics, financial modeling, 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 will be of great value to students, researchers and practitioners, who have some basic knowledge of probability.Arnaud Doucet received the Ph. D. degree from the University of Paris-XI Orsay in 1997. From 1998 to 2000, he conducted research at the Signal Processing Group of Cambridge University, UK. He is currently an assistant professor at the Department of Electrical Engineering of Melbourne University, Australia. His research interests include Bayesian statistics, dynamic models and Monte Carlo methods.Nando de Freitas obtained a Ph.D. degree in information engineering from Cambridge University in 1999. He is presently a research associate with the artificial intelligence group of the University of California at Berkeley. His main research interests are in Bayesian statistics and the application of on-line and batch Monte Carlo methods to machine learning.Neil Gordon obtained a Ph.D. in Statistics from Imperial College, University of London in 1993. He is with the Pattern and Information Processing group at the Defence Evaluation and Research Agency in the United Kingdom. His research interests are in time series, statistical data analysis, and pattern recognition with a particular emphasis on target tracking and missile guidance.|This volume presents results in a very active area of research of interest to statisticians, engineers, and computer scientists. The emphasis is on the applications of these important methods.
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
From the reviews:
JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
"...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)
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, have made it possible to solve numerically many complex, non-standarard problems that were previously intractable. This book presents the first comprehensive 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 will be of great value to students, researchers and practicioners, who have some basic knowledge of probability. Arnaud Doucet received the Ph. D. degree from the University of Paris- XI Orsay in 1997. From 1998 to 2000, he conducted research at the Signal Processing Group of Cambridge University, UK. He is currently an assistant professor at the Department of Electrical Engineering of Melbourne University, Australia. His research interests include Bayesian statistics, dynamic models and Monte Carlo methods. Nando de Freitas obtained a Ph.D. degree in information engineering from Cambridge University in 1999. He is presently a research associate with the artificial intelligence group of the University of California at Berkeley. His main research interests are in Bayesian statistics and the application of on-line and batch Monte Carlo methods to machine learning.
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.
From the reviews:
JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
"...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 techniquesdiscussed in this book are of great relevance to practitioners dealing with real time data." (Pradipta Sarkar, Technometrics, Vol. 45 (1), 2003)
Inhaltsverzeichnis
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
Klappentext
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, have made it possible to solve numerically
many complex, non-standarard problems that were previously
intractable.
This book presents the first comprehensive 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 will be of
great value to students, researchers and practicioners, who have some
basic knowledge of probability.
Arnaud Doucet received the Ph. D. degree from the University of Paris-
XI Orsay in 1997. From 1998 to 2000, he conducted research at the
Signal Processing Group of Cambridge University, UK. He is currently
an assistant professor at the Department of Electrical Engineering of
Melbourne University, Australia. His research interests include
Bayesian statistics, dynamic models and Monte Carlo methods.
Nando de Freitas obtained a Ph.D. degree in information engineering
from Cambridge University in 1999. He is presently a research
associate with the artificial intelligence group of the University of
California at Berkeley. His main research interests are in Bayesian
statistics and the application of on-line and batch Monte Carlo
methods to machine learning.
This volume presents results in a very active area of research of
interest to statisticians, engineers, and computer scientists. The
emphasis is on the applications of these important methods.
