Sequential Monte Carlo Methods in Practice (Hardcover)

Tutorial Chapter Particle Filters - A Theoretical Perspective Interacting Particle System Approximation Methods for Feynman-KacFormulae and Nonlinear Filtering Interacting Parallel Chains forSequential Bayesian Estimation Stochastic and Deterministic ParticleFilters Super-Efficient Particle Filters for Tracking Problems Following a Moving Target - Monte Carlo Inference for Dynamic BayesianModels Improvement Strategies for Particle Filters with Examplesfrom Communications and Audio Signal Processing Approximating andMaximizing the Likelihood for a General State Space Model Analysisand Implementation Issues of Regularized Particle Filters CombinedParameter and State Estimation in Simulation-based Filtering Sequential Importance Sampling Auxiliary Variable Based ParticleFilters Improved Particle Filters and Smoothing Terrain NavigationUsing Sequential Monte Carlo Methods Statistical Models of VisualShape and Motion Sequential Monte Carlo Methods for Neural Networks Short Term Forecasting of Electricity Load Particles and Mixturesfor Tracking and Guidance Monte Carlo Filter Approach to an Analysisof Small Count Time Series Monte Carlo Smoothing and Self-Organizing