Sun, 27 Jul 2008 08:27:29 BST CiteULike: mshafiei's hsmm http://www.citeulike.org/user/mshafiei/tag/hsmm CiteULike.org en-gb Copyright © 2004-2008 citeulike.org http://www.citeulike.org/user/mshafiei/article/2480107 <i>(2005), pp. 11-20.</i> Topic transition detection using hierarchical hidden Markov and semi-Markov models Dinh Phung TV Duong S Venkatesh Hung Bui doi:10.1145/1101149.1101153 (2005), pp. 11-20. 2008-03-06T19:27:27-00:00 2005 11 20 ACM hsmm topic-shift http://www.citeulike.org/user/mshafiei/article/2486593 <i>pp. 604-639.</i><br /><br />This article addresses the estimation of hidden semi-Markov chains from nonstationary discrete sequences. Hidden semi-Markov chains are particularly useful to model the succession of homogeneous zones or segments along sequences. A discrete hidden semi-Markov chain is composed of a nonobservable state process, which is a semi-Markov chain, and a discrete output process. Hidden semi-Markov chains generalize hidden Markov chains and enable the modeling of various durational structures. From an algorithmic point of view, a new forward-backward algorithm is proposed whose complexity is similar to that of the Viterbi algorithm in terms of sequence length (quadratic in the worst case in time and linear in space). This opens the way to the maximum likelihood estimation of hidden semi-Markov chains from long sequences. This statistical modeling approach is illustrated by the analysis of branching and flowering patterns in plants. Estimating Hidden Semi-Markov Chains From Discrete Sequences Y Guedon pp. 604-639. 2008-03-07T18:38:57-00:00 604 639 hmm hsmm http://www.citeulike.org/user/mshafiei/article/2486567 <i></i><br /><br />The paper proposes a hidden semi-Markov model for breakpoint rainfall data that consist of both the times at which rain-rate changes and the steady rates between such changes. The model builds on and extends the seminal work of Ferguson (1980) on variable duration models for speech. For the rainfall data the observations are modelled as mixtures of log-normal distributions within unobserved states where the states evolve in time according to a semi-Markov process. For the latter, parametric forms need to be specified for the state transition probabilities and dwell-time distributions. Recursions for constructing the likelihood are developed and the EM algorithm used to fit the parameters of the model. The choice of dwell-time distribution is discussed with a mixture of distributions over disjoint domains providing a flexible alternative. The methods are also extended to deal with censored data. An application of the model to a large-scale bivariate dataset of breakpoint rainfall measurements at Wellington, New Zealand, is discussed. Fitting Hidden Semi-Markov Models to Breakpoint Rainfall Data John Sansom Peter Thomson 2008-03-07T18:35:19-00:00 hmm hsmm http://www.citeulike.org/user/mshafiei/article/2480117 <i>(2005), pp. 11-20.</i> Topic transition detection using hierarchical hidden Markov and semi-Markov models Dinh Phung TV Duong S Venkatesh Hung Bui doi:http://doi.acm.org/10.1145/1101149.1101153 (2005), pp. 11-20. 2008-03-06T19:32:34-00:00 2005 11 20 ACM hmm hsmm segmentation