Hidden Semi Markov Models for Multiple Observation Sequences: The mhsmm Package for R
作者： Jared O'Connell , Søren Højsgaard
关键词: Variable-order Markov model 、 Markov model 、 Mathematics 、 Maximum-entropy Markov model 、 Hidden semi-Markov model 、 Algorithm 、 Markov property 、 Machine learning 、 Hidden Markov model 、 Forward algorithm 、 Artificial intelligence 、 Markov chain
摘要: This paper describes the R package mhsmm which implements estimation and prediction methods for hidden Markov semi-Markov models multiple observation sequences. Such techniques are of interest when observed data is thought to be dependent on some unobserved (or hidden) state. Hidden only allow a geometrically distributed sojourn time in given state, while extend this by allowing an arbitrary distribution. We demonstrate software with simulation examples application involving modelling ovarian cycle dairy cows.
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