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:: Volume 2, Issue 2 (3-2009) ::
J. of Stat. Sci. 2009, 2(2): 131-148 Back to browse issues page
A Bidirectional Hidden Markov Model in Linear Memory
Nasim Ejlali, Hamid Pezeshk
Abstract:   (16828 Views)
Hidden Markov models are widely used in Bioinformatics. They are applied to protein sequence alignment, protein family annotation and gene-finding.The Baum-Welch training is an expectation-maximization algorithm for training the emission and transition probabilities of hidden Markov models. For very long training sequence, even the most efficient algorithms are memory-consuming. In this paper we discuss different approaches to decrease the memory use and compare the performance of different algorithms. In addition, we propose a bidirection algorithm with linear memory. We apply this algorithm to simulated data of protein profile to analyze the strength and weakness of the algorithm.
Keywords: Hidden Markov Models, Baum-Welch Algorithm, Bidirectional Model, Profile Hidden Markov Model, Linear Memory.
Full-Text [PDF 611 kb]   (3299 Downloads)    
Type of Study: Research | Subject: Probability & Stochastic Processes
Received: 2011/07/4 | Accepted: 2013/08/13
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Ejlali N, Pezeshk H. A Bidirectional Hidden Markov Model in Linear Memory. J. of Stat. Sci.. 2009; 2 (2) :131-148
URL: http://jss.irstat.ir/article-1-18-en.html


Volume 2, Issue 2 (3-2009) Back to browse issues page
مجله علوم آماری – نشریه علمی پژوهشی انجمن آمار ایران Journal of Statistical Sciences
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