Sequential Monte Carlo Methods in Practice Book

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
• Shastic 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