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Complete theory on Hidden Markov Models :

Description Hidden Markov Models (HMMs) are a class of models for mimicing the probability density of a sequence of observed symbols. They are essentially stochastic nite state machines which output a symbol each time they depart from a state. By specifying the state transistion probabilities between states and the symbol generation model for each state, we can attempt to capture the underlying structure in a large set of symbol strings. In general, the operational paradigm is as follows: select a starting state according to some xed probability distribution. At each time step, generate an output symbol by invoking the generative model of the current state, and then transition to a new state according to a static transistion probability matrix.

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Complete theory on Hidden Markov Models


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