2013, Knowledge Discovery and Modeling

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2013

Knowledge Discovery

Modeling

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.

KDD

KDD Testing Machine Learning Measurement

How search accelerates your path to “AI first”

CIO Business Intelligence

MARCH 26, 2025

The combination of AI and search enables new levels of enterprise intelligence, with technologies such as natural language processing (NLP), machine learning (ML)-based relevancy, vector/semantic search, and large language models (LLMs) helping organizations finally unlock the value of unanalyzed data. How did we get here?

Knowledge Discovery

Knowledge Discovery Cost-Benefit Enterprise Modeling

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Using Empirical Bayes to approximate posteriors for large "black box" estimators

How search accelerates your path to “AI first”

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