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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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Any good machine learning book should help you. For example, Kevin Murphy's Machine Learning: A Probabilistic Perspective and Koller and Friendman, Probabilistic Graphical Models, both have chapters on parameter estimation. |
