Hidden Markov Models: Biostatistical Applications
Gary A. Churchill · Wiley StatsRef: Statistics Reference Online · 2014
Abstract Hidden Markov models are a generalization of the finite mixture model that allow for dependence in a sequence of observations via Markovian dependence of an unobserved (hidden) sequence of states. Inference problems associated with hidden Markov models include parameter estimation and restoration of the hidden state sequence. The recursive algorithms employed to solve these problems are related to the Kalman filter and have spawned a wide variety of applications. Areas of application range from speech recognition and signal processing through analysis of molecular sequence data.