Empirical Bayes
from Linear Digressions Hosted by Katie Malone
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.
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Frequently asked questions
How long is this episode of Linear Digressions?
This episode is 18 minutes long.
When was this Linear Digressions episode published?
This episode was published on February 20, 2017.
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