Uploaded May 2018 | Updated September 2026, 2 hours ago
This video explains how we would determine a model’s fit to data under idealised conditions. This provides a benchmark metric for determining a model's out of sample predictive power, to which applied measures aspire.
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
This video explains how we would determine a model’s fit to data under idealised conditions. This provides a benchmark metric for determining a model's out of sample predictive power, to which applied measures aspire.
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
