astroABC : An Approximate Bayesian Computation Sequential Monte Carlo sampler for cosmological parameter estimation
Abstract
Given the complexity of modern cosmological parameter inference where we arefaced with nonGaussian data and noise, correlated systematics and multiprobecorrelated 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 multiprobe 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 scikitlearn's KDTree, modules forspecifying optimal covariance matrix for a componentwise or multivariatenormal perturbation kernel, output and restartmore »
 Authors:
 Publication Date:
 Research Org.:
 Fermi National Accelerator Lab. (FNAL), Batavia, IL (United States)
 Sponsoring Org.:
 USDOE Office of Science (SC), High Energy Physics (HEP) (SC25)
 OSTI Identifier:
 1423221
 Report Number(s):
 arXiv:1608.07606; FERMILABPUB16334A
Journal ID: ISSN 22131337; 1653537
 DOE Contract Number:
 AC0207CH11359
 Resource Type:
 Journal Article
 Resource Relation:
 Journal Name: Astronomy and Computing; Journal Volume: 19; Journal Issue: C
 Country of Publication:
 United States
 Language:
 English
 Subject:
 79 ASTRONOMY AND ASTROPHYSICS; 46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND TECHNOLOGY
Citation Formats
Jennings, E., and Madigan, M. astroABC : An Approximate Bayesian Computation Sequential Monte Carlo sampler for cosmological parameter estimation. United States: N. p., 2017.
Web. doi:10.1016/j.ascom.2017.01.001.
Jennings, E., & Madigan, M. astroABC : An Approximate Bayesian Computation Sequential Monte Carlo sampler for cosmological parameter estimation. United States. doi:10.1016/j.ascom.2017.01.001.
Jennings, E., and Madigan, M. Sat .
"astroABC : An Approximate Bayesian Computation Sequential Monte Carlo sampler for cosmological parameter estimation". United States.
doi:10.1016/j.ascom.2017.01.001. https://www.osti.gov/servlets/purl/1423221.
@article{osti_1423221,
title = {astroABC : An Approximate Bayesian Computation Sequential Monte Carlo sampler for cosmological parameter estimation},
author = {Jennings, E. and Madigan, M.},
abstractNote = {Given the complexity of modern cosmological parameter inference where we arefaced with nonGaussian data and noise, correlated systematics and multiprobecorrelated 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 multiprobe 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 scikitlearn's KDTree, modules forspecifying optimal covariance matrix for a componentwise 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 nonstandard prior PDFs, a module forspecifying a constant, linear, log or exponential tolerance level,welldocumented examples and sample scripts. This code is hosted online athttps://github.com/EliseJ/astroABC},
doi = {10.1016/j.ascom.2017.01.001},
journal = {Astronomy and Computing},
number = C,
volume = 19,
place = {United States},
year = {Sat Apr 01 00:00:00 EDT 2017},
month = {Sat Apr 01 00:00:00 EDT 2017}
}

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