Hidden Markov Model (HMM) The Hidden Markov Model (HMM) is a powerful statistical tool for modeling generative sequences that can be characterized by an underlying process generating an observable sequence. A hidden Markov model is a doubly stochastic process, with an underlying stochastic process that is not observable (hence the word hidden), but can be observed through another stochastic process that produces the sequence of observations. The hidden process consists of a set of states connected to each other by transitions with probabilities, while the observed process consists of a set of outputs or observations, each of which may be emitted by each state according to some probability density function (pdf). Depending on the nature of this pdf, several HMM classes can be distinguished. If the observations are naturally discrete or quantized using vector quantization. The Hidden Markov Model is a finite set of states, each of which is associated with a (generally multidimensional) probability distribution Transitions among the states are governed by a set of probabilities called transition probabilities. In a particular state an outcome or observation can be generated, according to the associated probability distribution. It is only the outcome, not the state visible to an external observer…