Speaker 1: Ryan Lafferty

Title

Nonlocal Priors for Bayesian Model Selection

Abstract

We introduce the theory of Bayesian hypothesis testing and model selection. Classically, so-called local priors have been used for the prior distribution under the alternative hypothesis. However, these suffer from both practical and philosophical limitations. To address these limitations, we discuss the theory of non-local priors, initiated by V.E. Johnson and David Rossell. We review recent results and literature relating to nonlocal priors for Bayesian model selection, particularly in the case of high-dimensional regression. Finally, we define a family of non-local priors for model selection of Gaussian directed acyclic graph models and discuss basic properties and theoretical results.

Speaker 2: Lara John

Title

Chaos in the Classroom

Abstract

Chaos theory can be applied to many different sciences. In this talk we define chaos theory and discuss the work of the scientist most commonly heralded as the founder. We then delve deeper into chaos theory as it relates specifically to classroom management in elementary aged children. We will then discuss some specific results when we fix one of our individual contributors and see how classroom management improves.