An introduction to Jeffreys priors - 3 @SpartacanUsuals
An introduction to Jeffreys priors - 3  @SpartacanUsuals
Uploaded May 2018 | Updated September 2026, 1 hour 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 - 3Sample balancing via stratification and matchingModel implied variance-covariance matrix of indicators (matrix form) - part 1An introduction to the Poisson distribution - 1Effective sample size: representing the cost of dependent samplingMaximum likelihood: Normal error distribution - estimator variance part 1Simultaneous equation models - an introductionAn introduction to numerical integration through Gaussian quadratureBob’s bees: the importance of using multiple bees (chains) to judge MCMC convergenceHow to use rejection sampling to uniformly sample within a cows boundariesMaximum likelihood: Normal error distribution - estimator variance part 3Why is a likelihood not a probability distribution?
Ben Lambert |

An introduction to Jeffreys priors - 3

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