Despite major methodological developments, Bayesian inference in Gaussian graphical models remains challenging in high dimension due to the tremendous size of the model space. This article proposes a ...

Articulate the primary interpretations of probability theory and the role these interpretations play in Bayesian inference Use Bayesian inference to solve real-world statistics and data science ...

This paper develops new econometric methods to infer hospital quality in a model with discrete dependent variables and non-random selection. Mortality rates in patient discharge records are widely ...

We consider Bayesian inferences on a type of multivariate median and the multivariate quantile functionals of a joint distribution using a Dirichlet process prior. Unlike univariate quantiles, the ...

Approach developed at the Texas A&M School of Public Health offers promising new knowledge on idiopathic pulmonary fibrosis pathways Texas A&M University A new statistical technique developed by a ...

This course introduces the theoretical, philosophical, and mathematical foundations of Bayesian Statistical inference. Students will learn to apply this foundational knowledge to real-world data ...