JSTOR Daily

Empirical Bayes and Conditional Inference with Many Nuisance Parameters

This paper concerns the use of empirical Bayes methods to improve the efficiency of a parameter of interest, θ, in the presence of many nuisance parameters, {φi}, one from each data stratum. A class ...
Diginomica

Can Bayesian Networks provide answers when Machine Learning comes up short? It's a question of probabilities

Machine Learning gets all the marketing hype, but are we overlooking Bayesian Networks? Here's a deeper look at why "Bayes Nets" are underrated - especially when it comes to addressing probability and ...
JSTOR Daily

Nonparametric Bayes inference on conditional independence

In many application areas, a primary focus is on assessing evidence in the data refuting the assumption of independence of Y and X conditionally on Z, with Y response variables, X predictors of ...
Scientific American

What is Bayes's theorem, and how can it be used to assign probabilities to questions such as the existence of God? What scientific value does it have?

Chris Wiggins, an associate professor of applied mathematics at Columbia University, offers this explanation. A patient goes to see a doctor. The doctor performs a test with 99 percent ...
Inverse

Make Better Decisions With Bayesian Probability, the Smart Way to Consider Risk

It’s estimated that human adults make about 35,000 decisions a day — the percentage of good decisions depends on the adult. These choices can be as banal as deciding to roll or crumple toilet paper or ...