For my graduate thesis I researched Hidden Markov Models, a type of Markov Models where states of model are not known. We can only observe signals, that only show indirect information about the system.
Through the paper, it presents Hidden Markov Models from their history, to their use in biology. A part of paper is dedicated to Hidden Markov Models described with Gaussian mixtures. For this models, use of Baum-Welch and Viterbi algorithm is shown.
There is also a part about time series and their properties. Financial time series are discussed separately and there is an example of application.
Also, I included my own example, where I show the method for calculating tranistion matrix.
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