Kaleido Scope

BhGLM- Bayesian hierarchical GLMs and survival models, with applications to genomics and epidemiology

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 ...
lse

Hierarchical Bayesian Approach for Modeling Spatiotemporal Variability in Flood Damage Processes

Flood damage processes are complex and vary between events and regions. State‐of‐the‐art flood loss models are often developed on the basis of empirical damage data from specific case studies and do ...
Massey University

Bayesian Computational Statistics: Towards Causal Thinking

This course covers the ideas underlying statistical modelling in science through the lens of causal thinking. We cover the implementation of these ideas through Bayesian computational methods and ...
CU Boulder News & Events

Modeling space-time variability using multivariate semi-Bayesian hierarchical framework for seasonal total and extreme precipitation in the U.S. Northern Great Plains

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 ...
JSTOR Daily

A Bayesian Hierarchical Model for Prediction of Latent Health States from Multiple Data Sources with Application to Active Surveillance of Prostate Cancer

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 ...
CU Boulder News & Events

Bayesian Hierarchical Network Model for Forecasting Daily River Stage in a River Network

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 ...