This study compares two different techniques in a time series small area application: state space models estimated with the Kalman filter with a frequentist approach to hyperparameter estimation, and ...

BhGLM is a freely available R package that implements Bayesian hierarchical modeling for high-dimensional clinical and genomic data. It consists of functions for setting up various Bayesian ...

A novel Bayesian Hierarchical Network Model (BHNM) is designed for ensemble predictions of daily river stage, leveraging the spatial interdependence of river networks and hydrometeorological variables ...

Stephen G. Walker, Bani K. Mallick, Hierarchical Generalized Linear Models and Frailty Models with Bayesian Nonparametric Mixing, Journal of the Royal Statistical Society. Series B (Methodological), ...

Bracken, Cameron 1 ; Rajagopalan, Balaji 2 ; Cheng, Linyin 3 ; Gangopadhyay, Subhrendu 4. 1 CU Boulder 2 CU Boulder 3 NOAA 4 USBR. We present a spatial Bayesian hierarchical model for seasonal extreme ...

Accordingly, determining parameter values that lead to adequate prior densities is not straightforward. In this study, I present a hierarchical Bayesian model for calibrating divergence time analyses ...

A strength of Bayesian hierarchical modeling is that it allows inclusion of diverse sources of information, but I’m sure other methods could do fine also, if set up appropriately. P.P.S.