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Continuous Time HMM

  • William Turin
Chapter
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Part of the Information Technology: Transmission, Processing and Storage book series (PSTE)

Abstract

In the previous chapters, we considered HMMs which are probabilistic functions of discrete Markov chains. In the continuous time HMM, observations depend on states of a continuous time Markov process. The continuous time models assume that during a small time interval the probability of a state change is also small. Therefore, if the time interval is small, the state transition probability matrixPis close to a unit matrixI.When we estimated the model parameters on the basis of experimental data in Sec. 1.5, we discovered that the diagonal elements of the transition probability matrices are close to one. For that reason, it was convenient to represent the models with the matricesR = P — I.The other consideration which must be taken into account is the accuracy of all the computations: we would rather express all the equations containingPin terms ofRto avoid computations with numbers having different precision. These and other considerations suggest that we might make another step and consider a limiting case of an HMM when the time unit becomes infinitesimally small. In the limit we obtain a continuous time HMM.

Keywords

Transition Probability Matrix Interarrival Time Continuous Time Model Discrete Time Markov Chain Single Server Queue 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Science+Business Media New York 2004

Authors and Affiliations

  • William Turin
    • 1
  1. 1.AT&T Labs—ResearchFlorham ParkNew JerseyUSA

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