Hidden Hybrid Markov/Semi-Markov Model Fitting
Description
Develops algorithms for fitting, prediction, simulation
and initialization of the following models
(1)- hidden hybrid Markov/semi-Markov model,
introduced by Guedon (2005) ,
(2)- nonparametric mixture of B-splines emissions (Langrock et al., 2015
),
(3)- regime switching regression model
(Kim et al., 2008 ) and auto-regressive
hidden hybrid Markov/semi-Markov model,
(4)- spline-based nonparametric
estimation of additive state-switching models
(Langrock et al., 2018 )
(5)- robust emission model proposed by
Qin et al, 2024
(6)- several emission distributions, including mixture of multivariate normal
(which can also handle missing data using EM algorithm) and multi-nomial emission
(for modeling polymer or DNA sequences)
(7)- tools for prediction of future state sequence, computing the score of a new sequence,
splitting the samples and sequences to train and test sets, computing the information measures of
the models, computing the residual useful lifetime (reliability) and many other useful tools ...
(read for more description: Amini et al., 2022 and its
arxiv version: ).