Maximum likelihood solution - PowerPoint PPT Presentation

Pattern Recognition and Machine Learning

Pattern Recognition and Machine Learning. Chapter 2: Probability distributions. Parametric Distributions. Basic building blocks: Need to determine given Representation: or ? Recall Curve Fitting. Binary Variables (1). Coin flipping: heads=1, tails=0

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Week 3

Week 3. Pattern Recognition and Machine Learning. Chapter 2: Probability distributions. Bayesian Inference for the Gaussian (1). Assume ¾ 2 is known. Given i.i.d . data , the likelihood function for ¹ is given by

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847 views • 63 slides

Expectation-Maximization for HMMs and Motif Discovery

Expectation-Maximization for HMMs and Motif Discovery. Yves Moreau. Overview. The general Expectation-Maxization algorithm EM interpretation of the Baum-Welch algorithm for the learning of HMMs MEME for motif discovery. The general EM algorithm. EM algorithm. Maximum likelihood estimation

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736 views • 43 slides

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