Sequential Monte Carlo methods in practice
著者
書誌事項
Sequential Monte Carlo methods in practice
（Statistics for engineering and information science）
Springer, c2010
- : [pbk.]
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注記
Bibliography: p. [553]-576
Includes index
Differs from in publication year
内容説明・目次
内容説明
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.
目次
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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