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June 13, 2018June 13, 2018 MACHINE LEARNING TOKYO

Machine Learning Tokyo

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Statistics: Maximum likelihood estimation 4 October 4, 2022 at 10:45 am – 11:45 am Online event These 4 lectures by Michal Fabinger cover a frequently used statistical method: maximum likelihood estimation. The concepts are introduced in an intuitive yet rigorous way. The topics include Likelihood and conditional likelihood and their maximization, maximum likelihood estimation of linear models, logitmodels, models/logistic regression, probit models, and Poissonregression, score functions, Fisher information and asymptoticconfidence intervals…

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