no code implementations • 24 Aug 2017 • Cetin Savkli, Jeffrey Lin, Philip Graff, Matthew Kinsey
We present a new method of generating mixture models for data with categorical attributes.
no code implementations • 23 Aug 2017 • Cetin Savkli, J. Ryan Carr, Philip Graff, Lauren Kennell
The problem of categorical data analysis in high dimensions is considered.
no code implementations • 25 Sep 2014 • John Veitch, Vivien Raymond, Benjamin Farr, Will M. Farr, Philip Graff, Salvatore Vitale, Ben Aylott, Kent Blackburn, Nelson Christensen, Michael Coughlin, Walter Del Pozzo, Farhan Feroz, Jonathan Gair, Carl-Johan Haster, Vicky Kalogera, Tyson Littenberg, Ilya Mandel, Richard O'Shaughnessy, Matthew Pitkin, Carl Rodriguez, Christian Röver, Trevor Sidery, Rory Smith, Marc Van Der Sluys, Alberto Vecchio, Will Vousden, Leslie Wade
We show that our implementation is able to correctly recover the parameters of compact binary signals from simulated data from the advanced GW detectors.
General Relativity and Quantum Cosmology High Energy Astrophysical Phenomena Instrumentation and Methods for Astrophysics
no code implementations • 3 Sep 2013 • Philip Graff, Farhan Feroz, Michael P. Hobson, Anthony N. Lasenby
We present the first public release of our generic neural network training algorithm, called SkyNet.
2 code implementations • 13 Oct 2011 • Philip Graff, Farhan Feroz, Michael P. Hobson, Anthony Lasenby
In this paper we present an algorithm for rapid Bayesian analysis that combines the benefits of nested sampling and artificial neural networks.