Stochastic dynamical systems arise in many scientific fields, such as asset prices in financial markets, neural activity in ...

This uncertainty primarily arises from the limitations in modeling gravitational wave signals. Just as accurately determining the location of an earthquake source requires precise models of the ...

Researchers at GWUniversity built an AI simulation of Federal Reserve committee meetings, showing political pressure ...

Bayesian inference provides a robust framework for combining prior knowledge with new evidence to update beliefs about uncertain quantities. In the context of statistical inverse problems, this ...

This is a preview. Log in through your library . Abstract This paper discusses Bayesian inference procedures for a normal dispersion matrix. Structural information for the prior mean of the dispersion ...

Dirichlet process (DP) priors are a popular choice for semiparametric Bayesian random effect models. The fact that the DP prior implies a non-zero mean for the random effect distribution creates an ...

A new statistical technique developed by a researcher at the Texas A&M University School of Public Health and colleagues elsewhere offers fresh insights into how diseases affect individual cells. This ...

We review Bayesian and Bayesian decision theoretic approaches to subgroup analysis and applications to subgroup-based adaptive clinical trial designs. Subgroup analysis refers to inference about ...

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 ...