The factor model is an important construct for both portfolio managers and researchers in modern finance. For practitioners, factor model coefficients are used to guide the construction of optimal ...

This is a preview. Log in through your library . Abstract Inferences about the parameters in the three-component hierarchical design model $y_{ijk} = \mu + a_i + b ...

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 ...

Clustering problems (including the clustering of individuals into outcrossing populations, hybrid generations, full-sib families and selfing lines) have recently received much attention in population ...

Bayesian estimation methods form a dynamic branch of statistical inference, utilising Bayes’ theorem to update probabilities in light of new evidence. This framework combines prior knowledge with ...

A research team introduces a hierarchical Bayesian spatial approach that integrates UAV and terrestrial LiDAR data to ...

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 ...

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 ...

Machine Learning gets all the marketing hype, but are we overlooking Bayesian Networks? Here's a deeper look at why "Bayes Nets" are underrated - especially when it comes to addressing probability and ...

This article was published in Scientific American’s former blog network and reflects the views of the author, not necessarily those of Scientific American I’m not sure when I first heard of Bayes’ ...