149 citations · 529 across the 35 of their papers we have counts for
4 papers · 1 filter
Modeling Non-Stationary Processes Through Dimension Expansion
Luke Bornn, Gavin Shaddick, James V Zidek
In this paper, we propose a novel approach to modeling nonstationary spatial fields. The proposed method works by expanding the geographic plane over which these processes evolve i…
Discussion of "Riemann manifold Langevin and Hamiltonian Monte Carlo methods'' by M. Girolami and B. Calderhead
Luke Bornn, Julien Cornebise, Gareth W. Peters
This technical report is the union of two contributions to the discussion of the Read Paper "Riemann manifold Langevin and Hamiltonian Monte Carlo methods" by B. Calderhead and M.…
Bayesian clustering in decomposable graphs
Luke Bornn, François Caron
In this paper we propose a class of prior distributions on decomposable graphs, allowing for improved modeling flexibility. While existing methods solely penalize the number of edg…
Grouping Priors and the Bayesian Elastic Net
Luke Bornn, Raphael Gottardo, Arnaud Doucet
In the literature surrounding Bayesian penalized regression, the two primary choices of prior distribution on the regression coefficients are zero-mean Gaussian and Laplace. While…