1 code implementation • 12 Jul 2022 • Derek Hansen, Ismael Mendoza, Runjing Liu, Ziteng Pang, Zhe Zhao, Camille Avestruz, Jeffrey Regier
We present a new probabilistic method for detecting, deblending, and cataloging astronomical sources called the Bayesian Light Source Separator (BLISS).
no code implementations • 8 Jul 2021 • Ryan Giordano, Runjing Liu, Michael I. Jordan, Tamara Broderick
Bayesian models based on the Dirichlet process and other stick-breaking priors have been proposed as core ingredients for clustering, topic modeling, and other unsupervised learning tasks.
3 code implementations • 4 Feb 2021 • Runjing Liu, Jon D. McAuliffe, Jeffrey Regier
In images collected by astronomical surveys, stars and galaxies often overlap visually.
4 code implementations • 15 Oct 2018 • Runjing Liu, Ryan Giordano, Michael. I. Jordan, Tamara Broderick
Bayesian models based on the Dirichlet process and other stick-breaking priors have been proposed as core ingredients for clustering, topic modeling, and other unsupervised learning tasks.
Methodology
1 code implementation • 10 Oct 2018 • Runjing Liu, Jeffrey Regier, Nilesh Tripuraneni, Michael. I. Jordan, Jon McAuliffe
We wish to compute the gradient of an expectation over a finite or countably infinite sample space having $K \leq \infty$ categories.
3 code implementations • 1 Jun 2018 • Ryan Giordano, Will Stephenson, Runjing Liu, Michael. I. Jordan, Tamara Broderick
This linear approximation is sometimes known as the "infinitesimal jackknife" in the statistics literature, where it is mostly used to as a theoretical tool to prove asymptotic results.
Methodology