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.

In those approaches, estimates for nonrespondents were calculated using an EM algorithm by maximizing a posterior distribution. As an extension of their earlier work, we develop a Bayesian ...

An-Shun Tai, George C. Tseng, Wen-Ping Hsieh, BAYICE, The Annals of Applied Statistics, Vol. 15, No. 1 (March 2021), pp. 391-411 ...

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 ...

BIT framework uses Bayesian hierarchical modeling, which assesses probabilities across multiple layers of evidence rather than evaluating isolated pieces of information.

A separate model is developed for each season to capture the unique spatial features prevalent in the precipitation field. This modeling framework offers a flexible approach to incorporate covariates ...

In the suggested hierarchical model, an expression QTL (eQTL) model (which is essentially our missing data model) is part of the larger cQTL model and it represents a Bayesian model-based method ...

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 ...