This C++ version of BAT is still being maintained, but addition of new features is unlikely. Check out our new incarnation, BAT.jl, the Bayesian analysis toolkit in Julia. In addition to Metropolis-Hastings sampling, BAT.jl supports Hamiltonian Monte Carlo (HMC) with automatic differentiation, automatic prior-based parameter space transformations, and much more. See the BAT.jl documentation.
Documentation
To get started, download the quickstart guide. For more details on the theory behind BAT, the features, the details of the implementation and the example of usage, head over to the introduction. Also, the examples distributed together with BAT contain a lot of in-line comments which should provide help for writing your own models and running with BAT. The tutorials contain walk-throughs on selected statistical problems with complete implementations in BAT. Finally, the reference guide is generated automatically from the source code and shows the documentation of all classes, methods, etc.
Download BAT quick start guide (PDF) - last update 19.01.2015
Download Short introduction to BAT (PDF) - last update 19.01.2015
| reference guide | tutorials | models |
| faq | talks/presentations |
Publications
- A. Caldwell, D. Kollar, K. Kröninger, BAT - The Bayesian Analysis Toolkit,
Comput. Phys. Commun. 180 (2009) 2197-2209 (ScienceDirect) [arXiv:0808.2552]
Use this reference to cite BAT
Proceedings
- F. Beaujean, A. Caldwell, D. Kollar and K. Kröninger, BAT: The Bayesian analysis toolkit,
J. Phys.: Conf. Ser. 331 (2011) 072040 (Journal link) - A. Caldwell, D. Kollar, K. Kröninger, BAT - The Bayesian Analysis Toolkit,
J. Phys.: Conf. Ser. 219 032013 (2010) (Journal link)
Cited in
- Related to statistics, tools and algorithms
- D. Casadei, K. Kröninger, Objective Bayesian analysis of counting experiments with correlated sources of background,
[arXiv:1504.02566] - K. Cranmer, Practical Statistics for the LHC,
[arXiv:1503.07622] - A. Caldwell, C. Liu, Target Density Normalization for Markov Chain Monte Carlo Algorithms,
[arXiv:1410.7149] - D. Casadei, Reference analysis of the signal + background model in counting experiments II. Approximate reference prior,
JINST 9 (2014) T10006 (Errata: JINST 10 (2015) E04001) (Journal link) [arXiv:1407.5893] - K. Kröninger, S. Schumann and B. Willenberg, (MC)**3 -- a Multi-Channel Markov Chain Monte Carlo algorithm for phase-space sampling,
Comput. Phys. Commun. 186 (2015) 1 (Journal link) [arXiv:1404.4328] - O. Behnke, K. Kröninger, G. Schott and T.Schörner-Sadenius (Eds.), Data Analysis in High Energy Physics,
Wiley-VCH, 1 edition (August 19, 2013) (Wiley) - G. Schott et al., RooStats for Searches,
Contributed to the PHYSTAT 2011 Workshop [arXiv:1203.1547] - F. Beaujean et al., p-values for Model Evaluation,
Phys. Rev. D 83 (2011) 012004 (Journal link) [arXiv:1011.1674] - S. I. Bityukov and N. V. Krasnikov, The use of statistical methods for the search for new physics at the LHC (in Russian),
Extended text of lectures given by S.I. Bityukov for students of MIPT and doctoral students of IHEP [arXiv:1107.3974] - L. Moneta et al., The RooStats Project,
PoS ACAT2010 (201) 057 (Conference link) [arXiv:1009.1003]
- F. Beaujean and A. Caldwell, A Test Statistic for Weighted Runs,
J. Stat. Plan. Inference 141 (2011) 3437 (Journal link) [arXiv:1005.3233] - L. Demortier, S. Jain and H. B. Prosper, Reference priors for high energy physics,
Phys. Rev. D 82 (2010) 034002 (Journal link) [arXiv:1002.1111] - D. Piparo, G. Schott and G. Quast, RooStatsCms: a tool for analysis modelling, combination and statistical studies,
J. Phys.: Conf.Ser. 219 (2010) 032034 (Journal link) [arXiv:0905.4623]
- D. Casadei, K. Kröninger, Objective Bayesian analysis of counting experiments with correlated sources of background,
- All citations (via Inspire): here.
Theses and projects related to BAT
- Stephan Jahn, Beyond-the-Standard-Model Contributions to Rare B Decays Analyzed with Variational-Bayes Enhanced Adaptive Importance Sampling,
Master's thesis, February 2015, TUM (.pdf) - Benjamin Willenberg, Monte Carlo Techniques for Phase Space Integration,
Master's thesis, July 2014, II.Physik-UniGö-MSc-2014/03 - Matthew Lim, Bayesian inference in rare processes - traditional and modern methods (MultiNest interface),
Summer project, July-September 2013, (.pdf) - Stephan Kaltenstadler, Novel Algorithms for Data Analysis - Evidence Calculation with Markov Chain Monte Carlo-Methods,
Bachelor thesis, August 2013, (.pdf) - Frederik Beaujean, A Bayesian analysis of rare B decays with advanced Monte Carlo methods,
PhD thesis, November 2012, Online version - Mai Xiang, Development of a Bayesian Analysis Toolkit - A new error band display style for distributions of numbers of events,
Bachelor thesis, June 2012, (.pdf) - Carsten Brachem, Implementation and test of a simulated annealing algorithm in the Bayesian Analysis Toolkit (BAT),
Bachelor thesis, July 2009, II.Physik-UniGoe-Bach2009/03, (.pdf)