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astroABC [electronic resource] : An Approximate Bayesian Computation Sequential Monte Carlo sampler for cosmological parameter estimation

Published
Washington, D.C. : United States. Dept. of Energy. High Energy Physics Division, 2017.
Oak Ridge, Tenn. : Distributed by the Office of Scientific and Technical Information, U.S. Dept. of Energy
Physical Description
pages 16-22 : digital, PDF file
Additional Creators
Fermi National Accelerator Laboratory, United States. Department of Energy. High Energy Physics Division, and United States. Department of Energy. Office of Scientific and Technical Information
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Summary
Given the complexity of modern cosmological parameter inference where we arefaced with non-Gaussian data and noise, correlated systematics and multi-probecorrelated data sets, the Approximate Bayesian Computation (ABC) method is apromising alternative to traditional Markov Chain Monte Carlo approaches in thecase where the Likelihood is intractable or unknown. The ABC method is called"Likelihood free" as it avoids explicit evaluation of the Likelihood by using aforward model simulation of the data which can include systematics. Weintroduce astroABC, an open source ABC Sequential Monte Carlo (SMC) sampler forparameter estimation. A key challenge in astrophysics is the efficient use oflarge multi-probe datasets to constrain high dimensional, possibly correlatedparameter spaces. With this in mind astroABC allows for massive parallelizationusing MPI, a framework that handles spawning of jobs across multiple nodes. Akey new feature of astroABC is the ability to create MPI groups with differentcommunicators, one for the sampler and several others for the forward modelsimulation, which speeds up sampling time considerably. For smaller jobs thePython multiprocessing option is also available. Other key features include: aSequential Monte Carlo sampler, a method for iteratively adapting tolerancelevels, local covariance estimate using scikit-learn's KDTree, modules forspecifying optimal covariance matrix for a component-wise or multivariatenormal perturbation kernel, output and restart files are backed up everyiteration, user defined metric and simulation methods, a module for specifyingheterogeneous parameter priors including non-standard prior PDFs, a module forspecifying a constant, linear, log or exponential tolerance level,well-documented examples and sample scripts. This code is hosted online athttps://github.com/EliseJ/astroABC
Report Numbers
E 1.99:arxiv:1608.07606
E 1.99: fermilab-pub-16-334-a
fermilab-pub-16-334-a
arxiv:1608.07606
Subject(s)
Note
Published through SciTech Connect.
04/01/2017.
"arxiv:1608.07606"
" fermilab-pub-16-334-a"
"1653537"
Astronomy and Computing 19 C ISSN 2213-1337 FT
Jennings, E.; Madigan, M.
Funding Information
AC02-07CH11359
View MARC record | catkey: 23494875