An introduction to Jeffreys priors - 2 @SpartacanUsuals
An introduction to Jeffreys priors - 2  @SpartacanUsuals
Uploaded May 2018 | Updated September 2026, 2 hours ago
These series of videos explain what is meant by Jeffreys priors as well as how they satisfy a particular notion of ‘uninformativeness’. This concept is explained through a simple Bernoulli example.

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
An introduction to Jeffreys priors - 2Explaining the Kullback-Liebler divergence through secret codesThe Rubin Causal model - an introductionAn introduction to discrete marginal probability distributionsThe ideal measure of a models predictive fitOrthogonal Projection Operator in Least Squares - part 1SURE estimator derivation - part 2Kronecker Matrix Product - propertiesWhat is meant by overfitting?The conditional independence assumption: introductionThe syllabus covered by the book and YouTube courseGeometric interpretation of Least Squares: geometrical derivation of estimator
Ben Lambert |

An introduction to Jeffreys priors - 2

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