Using bees to demonstrate the importance of overdispersed Markov chains in MCMC @SpartacanUsuals
Using bees to demonstrate the importance of overdispersed Markov chains in MCMC @SpartacanUsuals
Uploaded May 2018 | Updated September 2026, 2 hours ago
This video uses an analogy - the release of bees in a house of unknown shape - to explain the importance of overdispersed initialisation of Markov chains in MCMC.

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
Using bees to demonstrate the importance of overdispersed Markov chains in MCMCThe problem with discrete approximation to integrals or probability densitiesHow to derive a Gibbs sampling routine in generalDerivation of variance-covariance matrix in factor analysis - part 2Factor analysis: predicted variance and covariance of indicators - part 1Monte Carlo Simulation of Omitted Variable Bias in Least SquaresMonte Carlo Simulation for Ordinary Least SquaresHow to code up a bespoke probability density in StanMaximum likelihood estimation of factor analysis models - part 2Why we typically use dependent sampling to sample from the posteriorAn introduction to the Poisson distribution - 2Score test (Lagrange Multiplier test) - introduction
Ben Lambert |

Using bees to demonstrate the importance of overdispersed Markov chains in MCMC

SHARE TO X SHARE TO REDDIT SHARE TO FACEBOOK WALLPAPER