(ML 11.3) Frequentist risk, Bayesian expected loss, and Bayes risk

A simple way to visualize the relationships between the frequentist risk, Bayesian expected loss, and Bayes risk.

[@bztube888]

Great, intuitive explanation. Books like to attack with formulas before bother to try to explain the ideas behind them.

[@marvinpolo6106]

him: "what makes it confusing is a bunch of bad notation" *starts explaining by introducing bunch of notation" ...interesting

[@sairushikjasti660]

"what makes it confusing is a bunch of bad notations" me watching this video and not understanding a single notation, welp

[@frobeniusfg]

This is your last chance. After this there is no turning back. You take the red pill: the story ends, you wake up in your bed and believe whatever frequentists want you to believe. You take the blue pill: you stay in Bayesland and I show you how deep the rabbit hole goes.

[@jiatianxu757]

Actually, we usually care about L(y-hat, y) which is L(f(theta hat), y)

[@jinghuayan5128]

what do you mean by average over theta? I am a little clueless about this. Looking forward to hear from you, thank you!

[@yugiohgx114]

Average over theta means that we multiply the loss function L(theta, f(D)) by the posterior probabilities p(theta | D) for all values of theta

[@mojomagoojohnson]

Great!

[@danielmburu6936]

hello how can i get the posterior distribution of the poisson given the prior as e raised to power labda

[@jiatianxu757]

P(theta|D) is proportional for P(D|theta) * P(theta), in your case, it will be the Poisson mass function given theta * the prior dist.

[@auggiewilliams3565]

poor explanation