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, ...

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 ...

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 ...

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 ...

This study models graduation rates at 4-year broad access institutions (BAIs). We examine the student body, structural-demographic, and financial characteristics that best predict 6-year graduation ...

Dr. James McCaffrey of Microsoft Research says the main advantage of using Gaussian naive Bayes classification compared to other techniques like decision trees or neural networks is that you don't ...

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 ...