Sequential Monte Carlo Methods in Practice

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.

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 Includes supplementary material: sn.pub/extras

Inhalt
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 RESAMPLEMOVE 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.