Sequential Monte Carlo Methods in Practice
Título:
Sequential Monte Carlo Methods in Practice
ISBN:
9781475734379
Edição:
1st ed. 2001.
PRODUCTION_INFO:
New York, NY : Springer New York : Imprint: Springer, 2001.
Descrição Física:
XXVIII, 582 p. online resource.
Série:
Information Science and Statistics,
Conteúdo:
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.
Resumo:
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.
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Acesso Eletrônico:
Full Text Available From Springer Nature Computer Science Archive Packages
LANGUAGE:
Inglês