Lecture

Maximum Likelihood: Inference and Model Comparison

In course

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Description

This lecture covers the concept of maximum likelihood inference, focusing on model selection and parameter estimation. It delves into the process of inferring probability distributions from data, emphasizing the importance of choosing the right model. The lecture also explores the challenges of comparing different models using likelihood ratios and the implications of prior knowledge in Bayesian inference.

Instructor

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Official source

https://mediaspace.epfl.ch/media/0_glbi57js

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Ontological neighbourhood

Statistics

Statistical inference: Statistical hypothesis testing, Mathematical statistics

Data analysis: Cross-validation

Information engineering

Data science: Topics in data science

Computer engineering

Databases: Relational databases

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