Sequential Monte Carlo Methods in Practice / Edition 1

Sequential Monte Carlo Methods in Practice / Edition 1

by Arnaud Doucet
     
 

ISBN-10: 1441928871

ISBN-13: 9781441928870

Pub. Date: 12/01/2010

Publisher: Springer New York

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.  See more details below

Overview

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.

Product Details

ISBN-13:
9781441928870
Publisher:
Springer New York
Publication date:
12/01/2010
Series:
Information Science and Statistics Series
Edition description:
Softcover reprint of hardcover 1st ed. 2001
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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