149 citations · 529 across the 35 of their papers we have counts for
9 papers · 1 filter
A Nonparametric Bayesian Methodology for Regression Discontinuity Designs
Zach Branson, Maxime Rischard, Luke Bornn +1
One of the most popular methodologies for estimating the average treatment effect at the threshold in a regression discontinuity design is local linear regression (LLR), which plac…
Nonparametric hierarchical Bayesian quantiles
Luke Bornn, Neil Shephard, Reza Solgi
Here we develop a method for performing nonparametric Bayesian inference on quantiles. Relying on geometric measure theory and employing a Hausdorff base measure, we are able to sp…
Moment conditions and Bayesian nonparametrics
Luke Bornn, Neil Shephard, Reza Solgi
Models phrased though moment conditions are central to much of modern inference. Here these moment conditions are embedded within a nonparametric Bayesian setup. Handling such a mo…
Fast and optimal nonparametric sequential design for astronomical observations
Justin J. Yang, Xufei Wang, Pavlos Protopapas +1
The spectral energy distribution (SED) is a relatively easy way for astronomers to distinguish between different astronomical objects such as galaxies, black holes, and stellar obj…
Forecasting with Historical Data or Process Knowledge under Misspecification: A Comparison
Luke Bornn, Marian Anghel, Ingo Steinwart
When faced with the task of forecasting a dynamic system, practitioners often have available historical data, knowledge of the system, or a combination of both. While intuition dic…
Sparsity-Promoting Bayesian Dynamic Linear Models
François Caron, Luke Bornn, Arnaud Doucet
Sparsity-promoting priors have become increasingly popular over recent years due to an increased number of regression and classification applications involving a large number of pr…