Lecture

Maximum Likelihood Estimation

Description

This lecture covers the concept of maximum likelihood estimation, which is a general method for estimating parameters in statistical models. It includes discussions on Bernoulli distribution, log-likelihood, Fisher information, bias, variance, and mean squared error. The instructor explains the importance of maximizing the log-likelihood function, calculating observed and expected information, and assessing the consistency of estimators.

Instructors (2)

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Official source

https://mediaspace.epfl.ch/media/0_afdzi8t3

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Ontological neighbourhood

Statistics

Statistical inference: Mathematical statistics, Statistical hypothesis testing

Data analysis: Regression analysis

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