mixdiagmvnorm_mstep

The M step function of the EM algorithm for the mixture
of multivariate normals with diagonal covariance matrix
as the emission distribution using the
observation matrix and the estimated weight vectors

Develops algorithms for fitting, prediction, simulation
and initialization of the following models
(1)- hidden hybrid Markov/semi-Markov model,
introduced by Guedon (2005) <doi:10.1016/j.csda.2004.05.033>,
(2)- nonparametric mixture of B-splines emissions (Langrock et al., 2015
<doi:10.1111/biom.12282>),
(3)- regime switching regression model
(Kim et al., 2008 <doi:10.1016/j.jeconom.2007.10.002>) and auto-regressive
hidden hybrid Markov/semi-Markov model,
(4)- spline-based nonparametric
estimation of additive state-switching models
(Langrock et al., 2018 <doi:10.1111/stan.12133>)
(5)- robust emission model proposed by
Qin et al, 2024 <doi:10.1007/s10479-024-05989-4>
(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 <doi:10.1007/s00180-022-01248-x> and its
arxiv version: <doi:10.48550/arXiv.2109.12489>).

Morteza Amini

hhsmm

Hidden Hybrid Markov/Semi-Markov Model Fitting

Afarin Bayat

Reza Salehian

mixdiagmvnorm_mstep function

<dl><dt>x</dt>
<dd>the observation matrix</dd>
<dt>wt1</dt>
<dd>the state probabilities matrix (number of observations
times number of states)</dd>
<dt>wt2</dt>
<dd>the mixture components probabilities list (of length
nstate) of matrices (number of observations times number of
mixture components)</dd></dl>

Arguments

Morteza Amini, <a href="/link/morteza.amini%40ut.ac.ir?package=hhsmm&version=0.4.2" data-mini-rdoc="hhsmm::morteza.amini@ut.ac.ir">morteza.amini@ut.ac.ir</a>

Author

the M step function of the EM algorithm — mixdiagmvnorm_mstep

<dl>

<dt>x</dt>
<dd>the observation matrix</dd>


<dt>wt1</dt>
<dd>the state probabilities matrix (number of observations
times number of states)</dd>


<dt>wt2</dt>
<dd>the mixture components probabilities list (of length
nstate) of matrices (number of observations times number of
mixture components)</dd>

</dl>

Morteza Amini, <a href='mailto:morteza.amini@ut.ac.ir'>morteza.amini@ut.ac.ir</a>

mixdiagmvnorm_mstep: the M step function of the EM algorithm

Description

Usage

Value

Arguments

Author

Examples