Empirical Bayes is a versatile approach to “learn from a lot” in two ways: first, from a large number of variables and, second, from a potentially large amount of prior information, for example, ...

Artificial intelligence can solve problems at remarkable speed, but it's the people developing the algorithms who are truly driving discovery. At The University of Texas at Arlington, data scientists ...

This paper extends the Bayesian Model Averaging framework to panel data models where the lagged dependent variable as well as endogenous variables appear as regressors. We propose a Limited ...

Here’s our estimate of public support for vouchers, broken down by religion/ethnicity, income, and state: (Click on image to see larger version.) We’re mapping estimates from a hierarchical Bayes ...

We estimate by Bayesian inference the mixed conditional heteroskedasticity model of Haas et al. (2004a Journal of Financial Econometrics 2, 211–50). We construct a Gibbs sampler algorithm to compute ...

A novel Bayesian Hierarchical Network Model (BHNM) is designed for ensemble predictions of daily river stage, leveraging the spatial interdependence of river networks and hydrometeorological variables ...

Dr. James McCaffrey of Microsoft Research shows how to predict a person's sex based on their job type, eye color and country of residence. Naive Bayes classification is a classical machine learning ...

At the time of writing, the details of the US-Iran ceasefire remain unclear. Shipping companies such as Maersk have said they will exercise caution about resuming transit through the Strait of Hormuz.