3 papers
math.NA2022
On a linearization of quadratic Wasserstein distance
Philip Greengard, Jeremy G. Hoskins, Nicholas F. Marshall +1
This paper studies the problem of computing a linear approximation of quadratic Wasserstein distance . In particular, we compute an approximation of the negative homogeneous w…
stat.CO2021
Fast methods for posterior inference of two-group normal-normal models
Philip Greengard, Jeremy Hoskins, Charles C. Margossian +2
We describe a class of algorithms for evaluating posterior moments of certain Bayesian linear regression models with a normal likelihood and a normal prior on the regression coeffi…
stat.CO2020
A Fast Linear Regression via SVD and Marginalization
Philip Greengard, Andrew Gelman, Aki Vehtari
We describe a numerical scheme for evaluating the posterior moments of Bayesian linear regression models with partial pooling of the coefficients. The principal analytical tool of…