Kalman filtering: theory and practice
Kalman filtering: theory and practice
On sequential Monte Carlo sampling methods for Bayesian filtering
Statistics and Computing
Rao-Blackwellised Particle Filtering for Dynamic Bayesian Networks
UAI '00 Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence
A model-based approach to reactive self-configuring systems
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 2
Factored particles for scalable monitoring
UAI'02 Proceedings of the Eighteenth conference on Uncertainty in artificial intelligence
Continuous time bayesian networks
UAI'02 Proceedings of the Eighteenth conference on Uncertainty in artificial intelligence
Learning continuous time bayesian networks
UAI'03 Proceedings of the Nineteenth conference on Uncertainty in Artificial Intelligence
Data association for topic intensity tracking
ICML '06 Proceedings of the 23rd international conference on Machine learning
Continuous Time Bayesian Networks for Host Level Network Intrusion Detection
ECML PKDD '08 Proceedings of the European conference on Machine Learning and Knowledge Discovery in Databases - Part II
Efficient failure detection on mobile robots using particle filters with Gaussian process proposals
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
CTPPL: a continuous time probabilistic programming language
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Importance Sampling for Continuous Time Bayesian Networks
The Journal of Machine Learning Research
Intrusion detection using continuous time Bayesian networks
Journal of Artificial Intelligence Research
Mean Field Variational Approximation for Continuous-Time Bayesian Networks
The Journal of Machine Learning Research
| Hi-index | 0.00 |
We present the continuous-time particle filter (CTPF) - an extension of the discrete-time particle filter for monitoring continuous-time dynamic systems. Our methods apply to hybrid systems containing both discrete and continuous variables. The dynamics of the discrete state system are governed by a Markov jump process. Observations of the discrete process are intermittent and irregular. Whenever the discrete process is observed, CTPF samples a trajectory of the underlying Markov jump process. This trajectory is then used to estimate the continuous variables using the system dynamics determined by the discrete state in the trajectory. We use the unscented Kalman-Bucy filter to handle nonlinearities and continuous time. We present results showing that CTPF is more stable in its performance than discrete-time particle filtering, even when the discrete-time algorithm is allowed to update many more times than CTPF. We also present a method for online learning of the Markov jump process model that governs the discrete states.