Abstract
The paper proposes a new extension of Hidden Markov Models HMM for communication systems by allowing the Markovian transitions between the channel's states to be influenced by some external “catalyzers” e.g. environmental or experimental conditions. The stochastic influence of the catalyzers is expressed by multinomial link functions. We introduce a combined iterative training procedure, with the Baum--Welch algorithm as a framework, including some nested algorithms such as the Newton--Raphson and the Expectation--Maximization EM algorithms. The monotony of the log-likelihood function associated with our procedure is proven. A simulation study is provided in order to prove the good performances of the proposed combined iterative training procedure. We consider that the Multinomial HMM will be an important and useful extension of HMM in bioinformatics and biostatistics, due to the possible applications in modeling the “hidden” ion channels whose states could be influenced by external factors.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
