An algorithm for estimation of the parameters of a multiscale stochastic process based on scale-recursive dynamics on trees is presented. The expectation-maximization algorithm is used to provide maximum likelihood estimates for the general case of a nonhomogeneous tree with no fixed structure for the process dynamics. Experimental results are presented using synthetic data.
We present the design and development of a Hidden Markov Model for the division of news broadcasts into story segments. Model topology, and the textual features used, are discussed, together with the non-parametric estimation techniques that were employed for obtaining estimates for both transition and observation probabilities. Visualization methods developed for the analysis of system performance are also presented.
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