Bayesian modelling portfolio: price elasticity, MCMC, HMMs, GMM/EM, segment conversion, and a deployed Bayesian model with drift monitoring - sdiazricagno/bayesian-modelling ...
A new EEG and hierarchical Bayesian modeling study shows that task conditions dominate perceptual decisions in the ...
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 ...
Researchers have introduced a statistical model designed to predict disease from microbiome data pooled across many studies, ...
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 ...
This webinar explores how advanced Bayesian forecasting models can improve predictions of labour market trends and support more informed policy decisions. Bayesian methods are being increasingly used ...
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 ...
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