5 papers
Bayesian Nonparametric Detection of Anomalies in Multivariate Functional Data
Daniel Krasnov, David Stephens
Anomalies in functional data arise from rare or distinct processes that deviate from the dominant data-generating mechanism. Detecting such departures is essential in applications…
Singular Bayesian Neural Networks
Mame Diarra Toure, David A. Stephens
Bayesian neural networks promise calibrated uncertainty but require parameters for standard mean-field Gaussian posteriors. We argue this cost is often unnecessary, particu…
Posterior Uncertainty for Targeted Parameters in Bayesian Bootstrap Procedures
Magid Sabbagh, David A. Stephens
We propose a general method to carry out a valid Bayesian analysis of a finite-dimensional `targeted' parameter in the presence of a finite-dimensional nuisance parameter. We apply…
Bayesian measurement error modeling of latent time series structure to assess the impact of pollutants on health
Yanfei Qu, David A. Stephens
The association between levels of air pollution and mortality rate is well-established, but quantifying the magnitude of the effect is sometimes complicated by limitations in the d…
Computational Considerations for the Linear Model of Coregionalization
Renaud Alie, David A. Stephens, Alexandra M. Schmidt
In the last two decades, the linear model of coregionalization (LMC) has been widely used to model multivariate spatial processes. However, it can be a challenging task to conduct…