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- W2188980425 abstract "Sequences that can be assumed to have been gener- ated by a number of Markov models, whose outputs are randomly interleaved but where the actual sources are hidden, occur in a number of practical situations where data is captured as an unlabeled stream of events. We present a practical method for estimating model parameters on large data sets under the assumption that all sources are identical. Results on representa- tive examples are presented, together with a discussion on the accuracy and performance of the proposed estimation algorithms. Finally, we describe a real-world case study where we apply the technique to the sequence of events recorded in the technical support database of an IT vendor. I. INTRODUCTION" @default.
- W2188980425 created "2016-06-24" @default.
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- W2188980425 date "2009-01-01" @default.
- W2188980425 modified "2023-09-27" @default.
- W2188980425 title "Estimating the Parameters of Randomly Interleaved" @default.
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