We adapt a semi-Bayesian hierarchical modeling framework to jointly characterize the space–time variability of seasonal precipitation totals and precipitation extremes across the Northern Great Plains ...

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 ...

Japan is an archipelago with diverse climate zones and complex topography that is prone to heavy rain and flooding. Add the ...

A Bayesian hierarchical model was developed to estimate the parameters in a physiologically based pharmacokinetic (PBPK) model for chloroform using prior information and biomarker data from different ...

This is a preview. Log in through your library . Abstract In this article, we present a Bayesian hierarchical model for predicting a latent health state from longitudinal clinical measurements. Model ...

We present a spatial Bayesian hierarchical model for seasonal extreme precipitation. At the first level of hierarchy, the seasonal maximum precipitation (i.e. block maxima) at any location is assumed ...

This is a preview. Log in through your library . Abstract Mortality forecasts are typically limited in that they pertain only to national death rates, predict only all-cause mortality or do not ...

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 ...

Here’s our estimate of public support for vouchers, broken down by religion/ethnicity, income, and state: (Click on image to see larger version.) We’re mapping estimates from a hierarchical Bayes ...