Lecture
Optimization and Simulation
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.
Official source
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