Abstract
In this paper, we investigate a novel online estimation algorithm for dynamic Bayesian network (DBN) parameters, given as conditional probabilities. We sequentially update the parameter adjustment rule based on observation data. We apply our algorithm to two well known representations of DBNs: to a first-order Markov chain (MC) model and to a hidden Markov model (HMM). A sliding window allows efficient adaptive computation in real time. We also examine the stochastic convergence and stability of the learning algorithm.
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