Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Bayesian forecasting and dynamic models (2nd ed.)
Bayesian forecasting and dynamic models (2nd ed.)
Factorial Hidden Markov Models
Machine Learning - Special issue on learning with probabilistic representations
Probabilistic Networks and Expert Systems
Probabilistic Networks and Expert Systems
On sequential Monte Carlo sampling methods for Bayesian filtering
Statistics and Computing
Contour Tracking by Stochastic Propagation of Conditional Density
ECCV '96 Proceedings of the 4th European Conference on Computer Vision-Volume I - Volume I
Tractable inference for complex stochastic processes
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Stochastic simulation algorithms for dynamic probabilistic networks
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
Context-specific independence in Bayesian networks
UAI'96 Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence
Expectation Correction for Smoothed Inference in Switching Linear Dynamical Systems
The Journal of Machine Learning Research
Bridging the gap between feature- and grid-based SLAM
Robotics and Autonomous Systems
Swarm-supported outdoor localization with sparse visual data
Robotics and Autonomous Systems
Support vector machine adaptive control of nonlinear systems
ICIC'05 Proceedings of the 2005 international conference on Advances in Intelligent Computing - Volume Part II
Sequential support vector machine control of nonlinear systems by state feedback
ISNN'05 Proceedings of the Second international conference on Advances in Neural Networks - Volume Part III
Journal of Intelligent and Robotic Systems
Stable mapping using a hyper particle filter
RoboCup 2009
Map-Based multiple model tracking of a moving object
RoboCup 2004
SLAM and navigation in indoor environments
PSIVT'11 Proceedings of the 5th Pacific Rim conference on Advances in Image and Video Technology - Volume Part I
On accurate localization and uncertain sensors
International Journal of Intelligent Systems
Wireless Personal Communications: An International Journal
Cross-domain collaboration recommendation
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Fast Structured Prediction Using Large Margin Sigmoid Belief Networks
International Journal of Computer Vision
Runtime monitoring of stochastic cyber-physical systems with hybrid state
RV'11 Proceedings of the Second international conference on Runtime verification
Fuzzy spatial constraints and ranked partitioned sampling approach for multiple object tracking
Computer Vision and Image Understanding
Vehicle localization in VANETs using data fusion and V2V communication
Proceedings of the second ACM international symposium on Design and analysis of intelligent vehicular networks and applications
Automated Bayesian quality control of streaming rain gauge data
Environmental Modelling & Software
Mining diabetes complication and treatment patterns for clinical decision support
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
Lifelong localization in changing environments
International Journal of Robotics Research
Localization and navigation of the CoBots over long-term deployments
International Journal of Robotics Research
Expert Systems with Applications: An International Journal
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Particle filters (PFs) are powerful sampling-based inference/learning algorithms for dynamic Bayesian networks (DBNs). They allow us to treat, in a principled way, any type of probability distribution, nonlinearity and non-stationarity. They have appeared in several fields under such names as "condensation", "sequential Monte Carlo" and "survival of the fittest". In this paper, we show how we can exploit the structure of the DBN to increase the efficiency of particle filtering, using a technique known as Rao-Blackwellisation. Essentially, this samples some of the variables, and marginalizes out the rest exactly, using the Kalman filter, HMM filter, junction tree algorithm, or any other finite dimensional optimal filter. We show that Rao-Blackwellised particle filters (RBPFs) lead to more accurate estimates than standard PFs. We demonstrate RBPFs on two problems, namely non-stationary online regression with radial basis function networks and robot localization and map building. We also discuss other potential application areas and provide references to some finite dimensional optimal filters.