Hidden Markov Random Fields
نویسندگان
چکیده
A noninvertible function of a first order Markov process, or of a nearestneighbor Markov random field, is called a hidden Markov model. Hidden Markov models are generally not Markovian. In fact, they may have complex and long range interactions, which is largely the reason for their utility. Applications include signal and image processing, speech recognition, and biological modeling. We show that hidden Markov models are dense among essentially all finitestate discrete-time stationary processes and finite-state lattice-based stationary random fields. This leads to a nearly universal parameterization of stationary processes and stationary random fields, and to a consistent non-parametric estimator. We show the results of attempts to fit simple speech and texture patterns.
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