Learning Dynamic Bayesian Networks

@inproceedings{Ghahramani1997LearningDB,
  title={Learning Dynamic Bayesian Networks},
  author={Zoubin Ghahramani},
  booktitle={Summer School on Neural Networks},
  year={1997}
}
Suppose we wish to build a model of data from a finite sequence of ordered observations, {Y1, Y2, . . . , Yt}. In most realistic scenarios, from modeling stock prices to physiological data, the observations are not related deterministically. Furthermore, there is added uncertainty resulting from the limited size of our data set and any mismatch between our model and the true process. Probability theory provides a powerful tool for expressing both randomness and uncertainty in our model [23]. We… CONTINUE READING
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