Bob’s bees: the importance of using multiple bees (chains) to judge MCMC convergence @SpartacanUsuals

Uploaded May 2018 | Updated September 2026, 29 minutes ago

This video uses an analogy (the release of bees in a house of unknown shape) to convey the importance of using multiple Markov chains to judge convergence to a target distribution in MCMC routines.

Gelman and Rubin's article I refer to is "Inference from Iterative Simulation Using Multiple Sequences", Statistical Science, 1992, and is available from Project Euclid here: projecteuclid.org/download/pdf_1/euclid.ss/1177011136.

This video is part of a lecture course which closely follows the material covered in the book, "A Student's Guide to Bayesian Statistics", published by Sage, which is available to order on Amazon here: amazon.co.uk/Students-Guide-Bayesian-Statistics/dp/1473916364

For more information on all things Bayesian, have a look at: ben-lambert.com/bayesian/. The playlist for the lecture course is here: youtube.com/playlist?list=PLwJRxp3blEvZ8AKMXOy0fc0cqT61GsKCG&disable_polymer=true

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