Lecture

Statistical Inference: Model Selection and Nuisance Parameters

In course

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Description

This lecture covers statistical inference topics such as model selection using the Kullback-Leibler divergence, the Akaike information criterion, and the network information criterion. It also discusses dealing with nuisance parameters, modified profile likelihoods, and higher-order inference methods.

Instructor

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Official source

https://mediaspace.epfl.ch/media/0_g6esfman

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Ontological neighbourhood

Statistics

Statistical inference: Mathematical statistics, Statistical hypothesis testing

Data analysis: Cross-validation

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