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A hidden Markov model (HMM) is one of the important statistical tools used for analyzing and modeling the biological data. An HMM is used in many various fields of bioinformatics, for example it is used in the protein secondary structure prediction. Using some algorithms as Viterbi, Forward, Backward and Baum-Welch, one can address some questions about emission generation in HMM. In standard HMM the "t" th hidden state, given the "t-l"st hidden state, is independent of previous states and in every state, emissions are assumed to be independent. In this work the relations among states and emissions are considered. The dependencies between emissions have already been studied by other researchers. In this paper a new approach for consideration of dependencies among emissions is presented. We start with the use of the information of the left hand side of any emission and introduce a new model. We then take the information of the right hand side of any' emission into account. The mathematical structure of the model is presented and the modifications are discussed.
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