A hidden Markov model (HMM) has invisible or unobservable states, but a visit to these hidden states results in the recording of observation that is a probabilistic function of the state. Given a sequence of observations, the problem is to infer the dynamical system that had produced the given sequence. This inference will result in a model for the underlying process. The three basic problems of HMMs are given below:

*Scoring problem*: The target is to find the*probability of an observed sequence*with HMM already given.*Alignment problem*: Given a particular HMM, the target is to determine from an observation sequence the most likely sequence of underlying hidden states that might have generated ...

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