Using Empirical Bayes to approximate posteriors for large "black box" estimators
The Unofficial Google Data Science Blog
NOVEMBER 4, 2015
But most common machine learning methods don’t give posteriors, and many don’t have explicit probability models. More precisely, our model is that $theta$ is drawn from a prior that depends on $t$, then $y$ comes from some known parametric family $f_theta$. Here, our items are query-ad pairs. Calculate posterior quantities of interest.
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