Comparison of statistical sampling methods with ScannerBit, the GAMBIT scanning module

• Published in: The European physical journal. C, Particles and fields Vol. 77; no. 11; pp. 1 - 49
• Main Authors: Martinez, Gregory D., McKay, James, Farmer, Ben, Scott, Pat, Roebber, Elinore, Putze, Antje, Conrad, Jan
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.11.2017; Springer Nature B.V; SpringerOpen
• Subjects: Algorithms; Astronomy; Astrophysics and Cosmology; Bayesian analysis; Computer simulation; Dark matter; Elementary Particles; Evolution; Hadrons; Heavy Ions; Markov analysis; Markov chains; Measurement Science and Instrumentation; Monte Carlo simulation; Nuclear Energy; Nuclear Physics; Physics; Physics and Astronomy; Quantum Field Theories; Quantum Field Theory; Samplers; Sampling methods; Special Article - Tools for Experiment and Theory; Statistical methods; Statistical tests; String Theory
• ISSN: 1434-6044; 1434-6052; 1434-6052
• DOI: 10.1140/epjc/s10052-017-5274-y

We introduce ScannerBit, the statistics and sampling module of the public, open-source global fitting framework GAMBIT. ScannerBit provides a standardised interface to different sampling algorithms, enabling the use and comparison of multiple computational methods for inferring profile likelihoods, Bayesian posteriors, and other statistical quantities. The current version offers random, grid, raster, nested sampling, differential evolution, Markov Chain Monte Carlo (MCMC) and ensemble Monte Carlo samplers. We also announce the release of a new standalone differential evolution sampler, Diver, and describe its design, usage and interface to ScannerBit. We subject Diver and three other samplers (the nested sampler MultiNest, the MCMC GreAT, and the native ScannerBit implementation of the ensemble Monte Carlo algorithm T-Walk) to a battery of statistical tests. For this we use a realistic physical likelihood function, based on the scalar singlet model of dark matter. We examine the performance of each sampler as a function of its adjustable settings, and the dimensionality of the sampling problem. We evaluate performance on four metrics: optimality of the best fit found, completeness in exploring the best-fit region, number of likelihood evaluations, and total runtime. For Bayesian posterior estimation at high resolution, T-Walk provides the most accurate and timely mapping of the full parameter space. For profile likelihood analysis in less than about ten dimensions, we find that Diver and MultiNest score similarly in terms of best fit and speed, outperforming GreAT and T-Walk; in ten or more dimensions, Diver substantially outperforms the other three samplers on all metrics.