Say you're looking to use some Bayesian methods to estimate parameters of a system. You've got the normalization figured out, and the likelihood, but the prior... what should you use for a prior? Empirical Bayes has an elegant answer: look to your previous experience, and use past measurements as a starting point in your prior.

Scratching your head about some of those terms, and why they matter? Lucky for you, you're standing in front of a podcast episode that unpacks all of this.