Hierarchical Bayesian Modeling of Fluid‐Induced Seismicity

Hierarchical bayesian modeling of pharmacophores in bioinformatics.

One of the key ingredients in drug discovery is the derivation of conceptual templates called pharmacophores. A pharmacophore model characterizes the physicochemical properties common to all active molecules, called ligands, bound to a particular protein receptor, together with their relative spatial arrangement. Motivated by this important application, we develop a Bayesian hierarchical model ...

Bayesian hierarchical modeling of air-sea interaction

[1] By means of Bayesian hierarchical modeling (BHM), we develop a model for aspects of vigorous air-sea interactions on a basin scale. The approach relies on both physical reasoning and statistical techniques for data processing and uncertainty management. The theory and current practice of BHM of air-sea interaction physics will be introduced and demonstrated in this paper. The demonstration ...

Scalable Multidimensional Hierarchical Bayesian Modeling on Spark

We consider the problem of estimating occurrence rates of rare events for extremely sparse data using pre-existing hierarchies and selected features to perform inference along multiple dimensions. In particular, we focus on the problem of estimating click rates for {Advertiser, Publisher, User} tuples where both the Advertisers and the Publishers are organized as hierarchies that capture broad ...

Bayesian Meta-analysis with Hierarchical Modeling

Geo-level Bayesian Hierarchical Media Mix Modeling

Media mix modeling is a statistical analysis on historical data to measure the return on investment (ROI) on advertising and other marketing activities. Current practice usually utilizes data aggregated at a national level, which often suffers from small sample size and insufficient variation in the media spend. When sub-national data is available, we propose a geo-level Bayesian hierarchical m...