Bayesian spatial statistics and modeling represent a robust inferential framework where uncertainty in spatial processes is explicitly quantified through probability distributions. This approach ...

In the 20th-century statistics wars, Bayesians were underdogs. Now their methods may help speed treatments to market.

This course covers the ideas underlying statistical modelling in science through the lens of causal thinking. We cover the implementation of these ideas through Bayesian computational methods and ...

We discuss the development of a course in Bayesian statistics that began as an offering to statistics graduate students, evolved into a course for graduate students in other departments, then was ...

The primary goal of the trial was to optimize radiation therapy (RT) dose among three levels (low, standard, and high), given either with placebo (P) or an investigational agent (A), for treating ...

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 ...

It turns out that the old adage about statistics and damned lies wasn’t a joke. Sticks and stones may be bonebreakers, and words inflict no (physical) pain, but numbers can kill. In 2004, for instance ...