4 papers · 1 filter
Marginally-calibrated deep distributional regression
Nadja Klein, David J. Nott, Michael Stanley Smith
Deep neural network (DNN) regression models are widely used in applications requiring state-of-the-art predictive accuracy. However, until recently there has been little work on ac…
Bayesian Variable Selection for Non-Gaussian Responses: A Marginally Calibrated Copula Approach
Nadja Klein, Michael Stanley Smith
We propose a new highly flexible and tractable Bayesian approach to undertake variable selection in non-Gaussian regression models. It uses a copula decomposition for the joint dis…
Bayesian Inference for Regression Copulas
Michael Stanley Smith, Nadja Klein
We propose a new semi-parametric distributional regression smoother that is based on a copula decomposition of the joint distribution of the vector of response values. The copula i…
High-dimensional copula variational approximation through transformation
Michael Stanley Smith, Ruben Loaiza-Maya, David J. Nott
Variational methods are attractive for computing Bayesian inference for highly parametrized models and large datasets where exact inference is impractical. They approximate a targe…