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Sequential Monte Carlo Methods in Practice
An Introduction to Sequential Monte Carlo Methods
other
Author(s):
Arnaud Doucet
,
Nando Freitas
,
Neil Gordon
Publication date
(Print):
2001
Publisher:
Springer New York
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NeuroImaging Methods
Author and book information
Book Chapter
Publication date (Print):
2001
Pages
: 3-14
DOI:
10.1007/978-1-4757-3437-9_1
SO-VID:
961a4905-831f-4b4f-ba82-f5cc0bcde3ea
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Book chapters
pp. 3
An Introduction to Sequential Monte Carlo Methods
pp. 17
Particle Filters — A Theoretical Perspective
pp. 43
Interacting Particle Filtering With Discrete Observations
pp. 79
Sequential Monte Carlo Methods for Optimal Filtering
pp. 97
Deterministic and Stochastic Particle Filters in State-Space Models
pp. 117
RESAMPLE-MOVE Filtering with Cross-Model Jumps
pp. 139
Improvement Strategies for Monte Carlo Particle Filters
pp. 159
Approximating and Maximising the Likelihood for a General State-Space Model
pp. 177
Monte Carlo Smoothing and Self-Organising State-Space Model
pp. 197
Combined Parameter and State Estimation in Simulation-Based Filtering
pp. 225
A Theoretical Framework for Sequential Importance Sampling with Resampling
pp. 247
Improving Regularised Particle Filters
pp. 273
Auxiliary Variable Based Particle Filters
pp. 295
Improved Particle Filters and Smoothing
pp. 321
Posterior Cramér-Rao Bounds for Sequential Estimation
pp. 339
Statistical Models of Visual Shape and Motion
pp. 359
Sequential Monte Carlo Methods for Neural Networks
pp. 381
Sequential Estimation of Signals under Model Uncertainty
pp. 401
Particle Filters for Mobile Robot Localization
pp. 429
Self-Organizing Time Series Model
pp. 445
Sampling in Factored Dynamic Systems
pp. 465
In-Situ Ellipsometry Solutions Using Sequential Monte Carlo
pp. 479
Manoeuvring Target Tracking Using a Multiple-Model Bootstrap Filter
pp. 499
Rao-Blackwellised Particle Filtering for Dynamic Bayesian Networks
pp. 517
Particles and Mixtures for Tracking and Guidance
pp. 533
Monte Carlo Techniques for Automated Target Recognition
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