What is a conjugate prior?

This video provides a short introduction to the concept of 'conjugate prior distributions'; covering its definition, examples and why we may choose to specify a distribution that is conjugate to a given likelihood.
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: www.amazon.co.uk/Students-Gui...
For more information on all things Bayesian, have a look at: ben-lambert.com/bayesian/. The playlist for the lecture course is here: • A Student's Guide to B...

[@perkelele]

Just got your book in the mail last week. I never expected to already reach chapter 9. Really love the book. Cheers!

[@st0a]

Wow! Thanks a lot! I really like this explanation, especially because I started recapping the beta distribution to understand the Dirichlet distribution and it made sense that you mentioned beta.

[@nityasriram56]

this is great! also I'm huge fan of the book, everything is so much easier to understand!

[@shile1522]

Thanks, great explanation!!

[@ProfessionalTycoons]

thanks!

[@jemah6396]

Nice explanation!

[@pratikanand2200]

The posterior distribution p(θ | x) is in the same probability distribution family as the prior probability distribution p(θ), the prior and posterior are then called conjugate distributions, and the prior is called a conjugate prior for the likelihood function p(x | θ). Prior and Likelihood must have same functional form not exact same distribution.

3:34 → bernoulli likelihood