Sequential Monte Carlo methods in practice

Arnaud Doucet, Nando de Freitas, Neil Gordon, editors ; foreword by Adrian Smith

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.

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  • 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

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書名 Sequential Monte Carlo methods in practice
著作者等 De Freitas, Nando
Doucet, Arnaud
Gordon, Neil
Doucet A.
Freitas N.De
Smith Adrian
シリーズ名 Statistics for engineering and information science
出版元 Springer
刊行年月 c2001
ページ数 xxvii, 581 p.
大きさ 24 cm
ISBN 0387951466
NCID BA52865290
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言語 英語
出版国 アメリカ合衆国