Factorial Fractional Hidden Markov Models

摘要

Conventional hidden Markov models generally consist of a Markov chain observed through a linear map corrupted by additive Gaussian noise. A lesser known extension of this class of models, is the so called Factorial Hidden Model (FHMM). FHMM's also have numerous applications, notably in machine learning and speech recognition. In this article we consider FHMM's with additive fractional Gaussian noise in the observed process.