Hidden Markov Model (HMM) is a statistical Markov model in which the system being modeled is assumed to be a Markov process with unobservable (i.e. hidden) states. In simpler Markov models (like a Markov chain), the state is directly visible to the observer, and therefore the state transition probabilities areContinue Reading
Hidden states are the unknowns we try to detect or predict. The Hidden states have a relationship amongst themselves called the transition probabilities. Observations are the evidence variables that we have a priori. Observations and states have a relationship between them called the emission probabilities.Continue Reading
The Viterbi algorithm (using the maximum likelihood decoding (MLD) algorithm) is a dynamic programming algorithm for finding the most likely sequence of hidden states – called the Viterbi path in hidden Markov models (HMM)Continue Reading