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Found 6 papers, 5 papers with code
Scalable Bayesian Inference for Detection and Deblending in Astronomical Images
We present a new probabilistic method for detecting, deblending, and cataloging astronomical sources called the Bayesian Light Source Separator (BLISS).
Evaluating Sensitivity to the Stick-Breaking Prior in Bayesian Nonparametrics
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.
Variational Inference for Deblending Crowded Starfields
In images collected by astronomical surveys, stars and galaxies often overlap visually.
Evaluating Sensitivity to the Stick-Breaking Prior in Bayesian Nonparametrics
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
Rao-Blackwellized Stochastic Gradients for Discrete Distributions
We wish to compute the gradient of an expectation over a finite or countably infinite sample space having $K \leq \infty$ categories.
A Swiss Army Infinitesimal Jackknife
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
