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Minimal Realization Problems for Hidden Markov Models
[article]
2015
arXiv
pre-print
Consider a stationary discrete random process with alphabet size d, which is assumed to be the output process of an unknown stationary Hidden Markov Model (HMM). Given the joint probabilities of finite length strings of the process, we are interested in finding a finite state generative model to describe the entire process. In particular, we focus on two classes of models: HMMs and quasi-HMMs, which is a strictly larger class of models containing HMMs. In the main theorem, we show that if the
arXiv:1411.3698v2
fatcat:ogx2gqzeh5hqblslxjdpvgxr2q