Optimization and Simulation

In course

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Description

This lecture covers the Metropolis-Hastings algorithm, focusing on gradient-based approaches to bias the search towards higher likelihood values. It explains the reject criterion, Python code implementation, and practical considerations such as multiple starting points and stationarity detection.

Instructor

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Official source

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

About this result

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Ontological neighbourhood

Statistics

Statistical inference: Mathematical statistics, Statistical hypothesis testing

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