called a hidden Markov model or HMM the states of the Markov Chain are not measurable (hence hidden) instead, we see y0; y1; : : : yt is a noisy measurement of xt

Hidden Markov Model = Markov Network with Output Probabilities states not directly observable (= hidden)

Here we have to determine the best sequence of hidden states, the one that most likely produced word image. This is an application of Decoding problem.

probabilistic or statistical framework. It is thus the purpose of this paper to explain- what a hiddenJvlarkov model is, why it is appropriate for certain types of problem.

Finding the Hidden Sequence. Given A, B and a sequence of obseveration O. Find the hidden sequence S that is most likely to generate O, that is to find S∗ = argmax P(O|S, A, B)

To incorporate these, Hidden Markov Models (HMM's) have recently been applied to forecast and predict the stock market. We present the Maximum a Posteriori HMM approach for forecasting …

In the broadest sense of the word, a hidden Markov model is a Markov process that is split into two components: an observable component and an unobserv-able or ‘hidden’ component.