Hidden Semi Markov Models for Multiple Observation Sequences: The mhsmm Package for R
This paper describes the R package mhsmm which implements estimation and prediction methods for hidden Markov and semi-Markov models for multiple observation sequences. Such techniques are of interest when observed data is thought to be dependent on some unobserved (or hidden) state. Hidden Markov m...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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Foundation for Open Access Statistics
2011-03-01
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| Series: | Journal of Statistical Software |
| Subjects: | |
| Online Access: | http://www.jstatsoft.org/v39/i04/paper |
| _version_ | 1828516017205149696 |
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| author | Jared O'Connell Søren Højsgaard |
| author_facet | Jared O'Connell Søren Højsgaard |
| author_sort | Jared O'Connell |
| collection | DOAJ |
| description | This paper describes the R package mhsmm which implements estimation and prediction methods for hidden Markov and semi-Markov models for multiple observation sequences. Such techniques are of interest when observed data is thought to be dependent on some unobserved (or hidden) state. Hidden Markov models only allow a geometrically distributed sojourn time in a given state, while hidden semi-Markov models extend this by allowing an arbitrary sojourn distribution. We demonstrate the software with simulation examples and an application involving the modelling of the ovarian cycle of dairy cows. |
| first_indexed | 2024-12-11T18:21:09Z |
| format | Article |
| id | doaj.art-ed204a98bc644a68ac1115da2d5fe118 |
| institution | Directory Open Access Journal |
| issn | 1548-7660 |
| language | English |
| last_indexed | 2024-12-11T18:21:09Z |
| publishDate | 2011-03-01 |
| publisher | Foundation for Open Access Statistics |
| record_format | Article |
| series | Journal of Statistical Software |
| spelling | doaj.art-ed204a98bc644a68ac1115da2d5fe1182022-12-22T00:55:15ZengFoundation for Open Access StatisticsJournal of Statistical Software1548-76602011-03-013904Hidden Semi Markov Models for Multiple Observation Sequences: The mhsmm Package for RJared O'ConnellSøren HøjsgaardThis paper describes the R package mhsmm which implements estimation and prediction methods for hidden Markov and semi-Markov models for multiple observation sequences. Such techniques are of interest when observed data is thought to be dependent on some unobserved (or hidden) state. Hidden Markov models only allow a geometrically distributed sojourn time in a given state, while hidden semi-Markov models extend this by allowing an arbitrary sojourn distribution. We demonstrate the software with simulation examples and an application involving the modelling of the ovarian cycle of dairy cows.http://www.jstatsoft.org/v39/i04/paperduration densityEM algorithmhidden Markov modelRsojourn timeViterbi algorithm |
| spellingShingle | Jared O'Connell Søren Højsgaard Hidden Semi Markov Models for Multiple Observation Sequences: The mhsmm Package for R Journal of Statistical Software duration density EM algorithm hidden Markov model R sojourn time Viterbi algorithm |
| title | Hidden Semi Markov Models for Multiple Observation Sequences: The mhsmm Package for R |
| title_full | Hidden Semi Markov Models for Multiple Observation Sequences: The mhsmm Package for R |
| title_fullStr | Hidden Semi Markov Models for Multiple Observation Sequences: The mhsmm Package for R |
| title_full_unstemmed | Hidden Semi Markov Models for Multiple Observation Sequences: The mhsmm Package for R |
| title_short | Hidden Semi Markov Models for Multiple Observation Sequences: The mhsmm Package for R |
| title_sort | hidden semi markov models for multiple observation sequences the mhsmm package for r |
| topic | duration density EM algorithm hidden Markov model R sojourn time Viterbi algorithm |
| url | http://www.jstatsoft.org/v39/i04/paper |
| work_keys_str_mv | AT jaredoconnell hiddensemimarkovmodelsformultipleobservationsequencesthemhsmmpackageforr AT sørenhøjsgaard hiddensemimarkovmodelsformultipleobservationsequencesthemhsmmpackageforr |