29 search results - page 2 / 6 » Variational Bayesian Kalman filtering in dynamical tomograph... |
ECML
2007
Springer
13 years 11 months ago
2007
Springer
We introduce a modified Kalman filter that performs robust, real-time outlier detection, without the need for manual parameter tuning by the user. Systems that rely on high quali...
NN
1997
Springer
13 years 9 months ago
1997
Springer
Bayesian networks are directed acyclic graphs that represent dependencies between variables in a probabilistic model. Many time series models, including the hidden Markov models (H...
ISBI
2006
IEEE
13 years 11 months ago
2006
IEEE
Automated detection of stable fracture points in a sequence of Computed Tomography (CT) images is found to be a challenging task. In this paper, an innovative scheme for automatic...
UAI
2000
13 years 6 months ago
2000
Particle filters (PFs) are powerful samplingbased inference/learning algorithms for dynamic Bayesian networks (DBNs). They allow us to treat, in a principled way, any type of prob...
IBPRIA
2007
Springer
13 years 11 months ago
2007
Springer
In time-series analysis it is often assumed that observed data can be modelled as being derived from a number of regimes of dynamics, as e.g. in a Switching Kalman Filter (SKF) [1,...