1 code implementation • 12 Jun 2016 • Brendon J. Brewer, Daniel Foreman-Mackey
In probabilistic (Bayesian) inferences, we typically want to compute properties of the posterior distribution, describing knowledge of unknown quantities in the context of a particular dataset and the assumed prior information.
Computation Instrumentation and Methods for Astrophysics Data Analysis, Statistics and Probability
1 code implementation • 4 Aug 2015 • Brendon J. Brewer, David Huijser, Geraint F. Lewis
We introduce a Bayesian solution to the problem of inferring the density profile of strong gravitational lenses when the lens galaxy may contain multiple dark or faint substructures.
Instrumentation and Methods for Astrophysics Cosmology and Nongalactic Astrophysics Astrophysics of Galaxies Data Analysis, Statistics and Probability Applications
1 code implementation • 25 Nov 2012 • Brendon J. Brewer, Daniel Foreman-Mackey, David W. Hogg
We present and implement a probabilistic (Bayesian) method for producing catalogs from images of stellar fields.
Instrumentation and Methods for Astrophysics Data Analysis, Statistics and Probability Applications
2 code implementations • 12 Dec 2009 • Brendon J. Brewer, Livia B. Pártay, Gábor Csányi
We introduce a general Monte Carlo method based on Nested Sampling (NS), for sampling complex probability distributions and estimating the normalising constant.
Computation Instrumentation and Methods for Astrophysics Data Analysis, Statistics and Probability
