Hidden Markov models for speech recognition
Technometrics
Alignment of multiple proteins with an ensemble of Hidden Markov Models
International Journal of Data Mining and Bioinformatics
Joint Bayesian model selection and estimation of noisy sinusoidsvia reversible jump MCMC
IEEE Transactions on Signal Processing
Bayesian Monte Carlo estimation for profile hidden Markov models
Mathematical and Computer Modelling: An International Journal
Bayesian tree-structured image modeling using wavelet-domain hidden Markov models
IEEE Transactions on Image Processing
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A Profile Hidden Markov Model PHMM is a standard form of a Hidden Markov Models used for modelling protein and DNA sequence families based on multiple alignment. In this paper, we implement Baum-Welch algorithm and the Bayesian Monte Carlo Markov Chain BMCMC method for estimating parameters of small artificial PHMM. In order to improve the prediction accuracy of the estimation of the parameters of the PHMM, we classify the training data using the weighted values of sequences in the PHMM then apply an algorithm for estimating parameters of the PHMM. The results show that the BMCMC method performs better than the Maximum Likelihood estimation.