149 citations · 529 across the 35 of their papers we have counts for
6 papers · 1 filter
FastGP: An R Package for Gaussian Processes
Giri Gopalan, Luke Bornn
Despite their promise and ubiquity, Gaussian processes (GPs) can be difficult to use in practice due to the computational impediments of fitting and sampling from them. Here we dis…
Improving Simulated Annealing through Derandomization
Mathieu Gerber, Luke Bornn
We propose and study a version of simulated annealing (SA) on continuous state spaces based on -sequences. The parameter regulates the degree of ran…
The Use of a Single Pseudo-Sample in Approximate Bayesian Computation
Luke Bornn, Natesh Pillai, Aaron Smith +1
We analyze the computational efficiency of approximate Bayesian computation (ABC), which approximates a likelihood function by drawing pseudo-samples from the associated model. For…
PAWL-Forced Simulated Tempering
Luke Bornn
In this short note, we show how the parallel adaptive Wang-Landau (PAWL) algorithm of Bornn et al. (2013) can be used to automate and improve simulated tempering algorithms. While…
An Adaptive Interacting Wang-Landau Algorithm for Automatic Density Exploration
Luke Bornn, Pierre Jacob, Pierre Del Moral +1
While statisticians are well-accustomed to performing exploratory analysis in the modeling stage of an analysis, the notion of conducting preliminary general-purpose exploratory an…
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.…