Lecture

Entropy and Sampling Theory

Description

This lecture covers the concept of entropy, which measures the disorder of a random variable, and its applications in data science. It also delves into the Exponential Family and Sampling Theory, discussing the properties of exponential distributions and their parameterizations. The lecture explores how statistics derived from samples can provide insights into unknown parameters, emphasizing the importance of sampling distributions and ancillary statistics.

Instructors (2)

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Official source

https://mediaspace.epfl.ch/media/0_yddhe9ds

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Ontological neighbourhood

Statistics

Statistical inference: Mathematical statistics, Bayesian statistics

Data collection: Statistical sampling

Physics

Thermodynamics: Statistical mechanics

Information engineering

Information theory: Channel capacity

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