Information driven parameter dynamics on-line Bayesian learning with sequential Monte Carlo
2005
A new parameter dynamics that incorporates the information available for training instead of the standard "blind" parameter dynamics is proposed for on-line Bayesian learning. A significant improvement is realized over the schemes the authors have previously proposed. The particular advantage of the currently proposed approach is the speed at which it follows abrupt changes.
Keywords:
- Computer science
- Monte Carlo molecular modeling
- Temporal difference learning
- Wake-sleep algorithm
- Monte Carlo method
- Monte Carlo method in statistical physics
- Machine learning
- Markov chain Monte Carlo
- Bayesian statistics
- Particle filter
- Artificial intelligence
- Control theory
- Algorithm
- Hybrid Monte Carlo
- Bayesian inference
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