Alex Lenkoski

TL;DR: A comprehensive Bayesian approach for graphical model determination in observational studies that can accommodate binary, ordinal or continuous variables simultaneously simultaneously is proposed.

TL;DR: This work introduces efficient Markov chain Monte Carlo methods for inference and model determination in multivariate and matrix-variate Gaussian graphical models and extends their sampling algorithms to a novel class of conditionally autoregressive models for sparse estimation inMultivariate lattice data.

TL;DR: In this article, the authors use Bayesian Model Averaging (BMA) to resolve the model uncertainty that surrounds the validity of the competing FDI theories and identify the determinants of the margins of FDI (intensive and extensive), which are shown to differ profoundly.

TL;DR: This work proposes the use of a Gaussian copula, which offers a simple procedure for recovering the dependence that is lost in the estimation of the ensemble BMA marginals, and shows that it recovers many well‐understood dependencies between weather quantities and subsequently improves calibration and sharpness over both the raw ensemble and a method which does not incorporate joint distributional information.

TL;DR: In this paper, the authors use Bayesian Model Averaging (BMA) to resolve the model uncertainty that surrounds the validity of the competing FDI theories and identify the determinants of the margins of FDI (intensive and extensive), which are shown to differ profoundly.