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A hidden Markov model (HMM) is a statistical Markov model in which the system being modeled is assumed to be a Markov process with unobserved (hidden) states. A HMM can be considered the simplest dynamic Bayesian network. The mathematics behind the HMM was developed by L. E. Baum and coworkers.[1][2][3][4][5] It is closely related to an earlier work on optimal nonlinear filtering problem (stochastic processes) by Ruslan L. Hidden Markov model Hidden Markov model
Course Features Course Description This course is offered both to undergraduates (6.041) and graduates (6.431), but the assignments differ. 6.041/6.431 introduces students to the modeling, quantification, and analysis of uncertainty. Topics covered include: formulation and solution in sample space, random variables, transform techniques, simple random processes and their probability distributions, Markov processes, limit theorems, and elements of statistical inference. Electrical Engineering and Computer Science | 6 Electrical Engineering and Computer Science | 6