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I was reading about parameter estimation techniques such as Maximum Likelihood Estimation(MLE) and Expectation Maximization(EM). I tried to derive the Maximum Likelihood Estimation formula for Markov Chain and I failed miserably. I, then, found a reference that helped me to see the big picture.

What book/reference you recommend that could help me to learn how to approach optimization techniques that are common in parameter estimation for more general cases (e.g. Hidden Markov Model, Bayesian Network, Dynamic Bayesian Networks .. etc)?

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I find the text "Probabilistic Graphical Models: Principles and Techniques" by Daphne Koller and Nil Friedman to be a great reference book. Besides you can take the corresponding online free course offered by Coursera

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