This book's organization : read me first! -- Introduction : models we believe in -- What is this stuff called probability? -- Bayes' rule -- Inferring a binomial proportion via exact mathematical ...

We present a Bayesian hierarchical model for detecting differentially expressing genes that includes simultaneous estimation of array effects, and show how to use the output for choosing lists of ...

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 ...

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 ...

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 ...

What Is A Hierarchical Models? Hierarchical models, also known as hierarchical statistical models, multilevel models or random-effects models, are tools for analysing data with a nested or grouped ...

Aim: Our aim was to develop predictive statistical models for mapping the abundance of 18 waterfowl species at a pan-Canadian level. We refined the previous generation of national waterfowl models by ...

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 ...