Statistics and Data Analysis

Assigning this sampling distribution is only a means of describing our own
prior state of knowledge about the measurement errors

E. T. Jaynes (1994)

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1 - Books

A First Course on Time Series Analysis, S. Aulbach et al., SAS, 2006.
Statistical methods in experimental physics, Frederick James, World Scientific, 2006.
Bayesian Reasoning in Data Analysis, A Critical Introduction, G. D'Agostini, World Scientific, 2003.
Probability Theory: The Logic of Science, E. T. Jaynes, Cambridge University Press, 2003. Edited by G. Larry Bretthorst (See also the Fragmentary Edition of June 1994, available also here, and the Unofficial Errata and Commentary).
Kendall's Advanced Theory of Statistics, Volume 2A: Classical Inference and and the Linear Model, M. Kendall, A. Stuart, J.K. Ord, S. Arnold, Oxford University Press, 1999. Sixth Edition.
Statistical Data Analysis, Glen Cowan, Oxford University Press, 1998.
Kendall's Advanced Theory of Statistics, Volume 1: Distribution Theory, M. Kendall, A. Stuart, J.K. Ord, Oxford University Press, 1994. Sixth Edition.
Kendall's Advanced Theory of Statistics, Volume 2B: Bayesian Inference, A. O'Hagan, Oxford University Press, 1994. First Edition.
Probability and Statistics in Experimental Physics, Byron P. Roe, Springer, 1992.
Statistics: A Guide to the Use of Statistical Methods in the Physical Sciences, R. J. Barlow, Wiley, 1989.
Statistics for nuclear and particle physicists, Louis Lyons, Cambridge University Press, 1986.
Statistical Methods in Experimental Physics, W. T. Eadie, D. Drijard, F. E. James, M. Roos, B. Sadoulet, North Holland, 1971.
Theory of Probability, H. Jeffreys, Oxford University Press, 1961. First published in 1939.

2 - Reviews

Reproducibility and Replication of Experimental Particle Physics Results, Thomas R. Junk, Louis Lyons, arXiv:2009.06864, 2020.
21st Century Statistical and Computational Challenges in Astrophysics, Eric D. Feigelson, Rafael S. de Souza, Emille E. O. Ishida, Gutti Jogesh Babu, Ann.Rev.Stat.App. 8 (2021) 493-517, arXiv:2005.13025.
Combining parameter values or $p$-values, Louis Lyons, Emilien Chapon, arXiv:1704.05540, 2017.
Application of the Allan Variance to Time Series Analysis in Astrometry and Geodesy: A Review, Zinovy Malkin, arXiv:1607.04712, 2016.
Generalisations of Fisher Matrices, Alan Heavens, Entropy 18 (2016) 236, arXiv:1606.06455.
External observer reflections on QBism, Andrei Khrennikov, arXiv:1512.07195, 2015.
Introduction to Randomness and Statistics, Alexander K. Hartmann, arXiv:0910.4545, 2009.
The BLUE's covariance matrix revisited: A review, Jarkko Isotalo, Simo Puntanen, George P.H. Styan, Journal of Statistical Planning and Inference 138 (2008) 2722-2737.

3 - Reviews - Talks

PHYSTAT$\nu$ at CERN (January 2019), Louis Lyons, arXiv:1905.10323, 2019. NuPhys2018,`Prospects in Neutrino Physics', Cavendish Conference Centre, London, UK, December 19-21, 2018.
PhyStat-$\nu$ 2016 at the IPMU: Summary of Discussions, Yoshi Uchida, Mark Hartz, R. Phillip Litchfield, Callum Wilkinson, Asher Kaboth, arXiv:1806.10913, 2018.
Summary of the recent PHYSTAT-$\nu$ Workshops, Louis Lyons, arXiv:1705.01874, 2017. NuPhys2016 (London, 12-14 December 2016).
Three Lectures on Probability and Statistics, Carlos Mana, arXiv:1610.05590, 2016.
Statistical Issues in Neutrino Physics Analyses, Louis Lyons, arXiv:1607.03549, 2016. NuPhys2015 'Prospects in Neutrino Physics', December 2015.
Practical Statistics for Particle Physicists, Harrison B. Prosper, arXiv:1504.00945, 2015. 2012 European School of High-Energy Physics, La Pommeraye, Anjou, France, 6-19 June 2012.
Probability and Statistics for Particle Physicists, J. Ocariz, arXiv:1405.3402, 2014. First Asia-Europe-Pacific School of High-Energy Physics, Fukuoka, Japan, 14-27 October 2012.
Topics in statistical data analysis for high-energy physics, G. Cowan, arXiv:1012.3589, 2010. 2009 European School of High-Energy Physics, Bautzen, Germany, 14-27 Jun 2009.
Statistical methods in cosmology, Licia Verde, Lect. Notes Phys. 800 (2010) 147-177, arXiv:0911.3105. 2nd Trans-Regio Winter school in Passo del Tonale.
Statistical techniques in cosmology, Alan Heavens, arXiv:0906.0664, 2009. Francesco Lucchin summer school, Bertinoro, Italy, May 2009.
Basics of Feature Selection and Statistical Learning for High Energy Physics, Anselm Vossen, arXiv:0803.2344, 2008. Track 'Computational Intelligence for HEP Data Analysis' at iCSC 2006.
Conference Summary: Astronomy Perspective of Astro-Statistics, Ofer Lahav, ASP Conf.Ser. (2006), arXiv:astro-ph/0610713. Statistical Challenges in Modern Astronomy IV, 2007.
Blind Analysis in Particle Physics, Aaron Roodman, ECONF C030908 (2003) TUIT001, arXiv:physics/0312102. PHYSTAT2003, SLAC, Stanford, Ca, USA, 8-11 Sep 2003.
Introduction to Statistical Issues in Particle Physics, Roger Barlow, ECONF C030908 (2003) MOAT002, arXiv:physics/0311105. PHYSTAT2003, SLAC, Stanford, Ca, USA, 8-11 Sep 2003.
Statistical problems in particle physics, astrophysics and cosmology., L. Lyons (ed.), R. P. Mount (ed.), R. Reitmeyer (ed.), 2003. PHYSTAT2003: Statistical Problems in Particle Physics, Astrophysics, and Cosmology, SLAC, Stanford, Ca, USA, 8-11 Sep 2003.
Conference Summary Talk, R. Cousins, 2002. Advanced Statistical Techniques in Particle Physics, Durham, UK, 18-22 March 2002.
A Glossary of Selected Statistiscal Terms, H.B. Prosper, J.T. Linneman, W.A. Rolke, 2002. Advanced Statistical Techniques in Particle Physics, Durham, UK, 18-22 March 2002.
Advanced statistical techniques in particle physics., M. R. Whalley (ed.), L. Lyons (ed.), 2002. Conference on Advanced Statistical Techniques in Particle Physics, Durham, England, 18-22 Mar 2002.
Introduction to Monte Carlo methods, Stefan Weinzierl, arXiv:hep-ph/0006269, 2000.
Workshop on Confidence Limits, F. James (ed.), L. Lyons (ed.), Y. Perrin (ed.), 2000. CERN 'Yellow' Report CERN 2000-005. Workshop on Confidence Limits at CERN, 17-18 January 2000.

4 - Reviews - Bayesian Theory

Nested sampling for physical scientists, Greg Ashton et al., Nature 2 (2022) 39, arXiv:2205.15570.
Markov Chain Monte Carlo Methods for Bayesian Data Analysis in Astronomy, Sanjib Sharma, Ann.Rev.Astron.Astrophys. 55 (2017) 213, arXiv:1706.01629.
Bayesian astrostatistics: a backward look to the future, Thomas J. Loredo, arXiv:1208.3036, 2012.
Bayesian inference in physics, Udo von Toussaint, Rev. Mod. Phys. 83 (2011) 943-999, American Physical Society.
Bayes in the sky: Bayesian inference and model selection in cosmology, Roberto Trotta, Contemp. Phys. 49 (2008) 71-104, arXiv:0803.4089.
Bayesian inference in processing experimental data: Principles and basic applications, G. D'Agostini, Rept. Prog. Phys. 66 (2003) 1383-1420, arXiv:physics/0304102.
Bayesian reasoning in high-energy physics: Principles and applications, G. D'Agostini, 1999. CERN 'Yellow' report CERN 99-03.
Formal Rules for Selecting Prior Distributions: A Review and Annotated Bibliography, R. E. Kass, L. Wasserman, Journal of The American Statistical Association 91 (1996) 1343.
Probability and Measurement Uncertainty in Physics - a Bayesian Primer, G. D'Agostini, arXiv:hep-ph/9512295, 1995.
The Return of the Prodigal: Bayesian Inference in Astrophysics, Thomas J. Loredo, 1994.
The Promise of Bayesian Inference for Astrophysics, T. J. Loredo, 1992. in Statistical Challenges in Modern Astronomy, ed. E.D. Feigelson and G.J. Babu, Springer-Verlag, New York, 1992, pp. 275-297.
From Laplace to Supernova SN 1987A: Bayesian Inference in Astrophysics, T. J. Loredo, 1990. in Maximum-Entropy and Bayesian Methods, Dartmouth, 1989, ed. P. Fougere, Kluwer Academic Publishers, Dordrecht, The Netherlands, 1990, pp. 81-142.

5 - Reviews - Bayesian Theory - Talks

Lectures on Probability, Entropy, and Statistical Physics, Ariel Caticha, arXiv:0808.0012, 2008. MaxEnt 2008, the 28th International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering (July 8-13, 2008, Boraceia Beach, Sao Paulo, Brazil).
Bayesian analysis, Harrison B. Prosper, arXiv:hep-ph/0006356, 2000. Yellow Report CERN 2000-005, p.29-47. Workshop on Confidence Limits, CERN, 17-18 January 2000.

6 - Reviews - Frequentist Statistics

P-values: misunderstood and misused, Bertie Vidgen, Taha Yasseri, arXiv:1601.06805, 2016.
Aspects of likelihood inference, Nancy Reid, arXiv:1309.7816, 2013.
$\chi^2$ and Linear Fits, A. Gould, arXiv:astro-ph/0310577, 2003.

7 - Articles

A Cautionary Tale of Decorrelating Theory Uncertainties, Aishik Ghosh, Benjamin Nachman, Eur.Phys.J.C 82 (2022) 46, arXiv:2109.08159.
Combined Neyman-Pearson Chi-square: An Improved Approximation to the Poisson-likelihood Chi-square, Xiangpan Ji, Wenqiang Gu, Xin Qian, Hanyu Wei, Chao Zhang, Nucl.Instrum.Meth. A961 (2020) P163677, arXiv:1903.07185.
A unified perspective on modified Poisson likelihoods for limited Monte Carlo data, Thorsten Glusenkamp, JINST 15 (2020) P01035, arXiv:1902.08831.
A binned likelihood for stochastic models, Carlos A. Arguelles, Austin Schneider, Tianlu Yuan, JHEP 1906 (2019) 030, arXiv:1901.04645.
The Simplified Likelihood Framework, Andy Buckley et al., JHEP 1904 (2019) 064, arXiv:1809.05548.
Statistical Significance of CP Violation in Long Baseline Neutrino Experiments, Walter Toki, Thomas W. Campbell, Erez Reinherz-Aronis, Nucl.Instrum.Meth. A921 (2019) 71-80, arXiv:1806.05266.
Learning New Physics from a Machine, Raffaele Tito D'Agnolo, Andrea Wulzer, Phys.Rev. D99 (2019) 015014, arXiv:1806.02350.
A model independent safeguard for unbinned Profile Likelihood, Nadav Priel, Ludwig Rauch, Hagar Landsman, Alessandro Manfredini, Ranny Budnik, JCAP 1705 (2017) 013, arXiv:1610.02643.
Analysis of distorted measurements - parameter estimation and unfolding, Guenter Zech, arXiv:1607.06910, 2016.
On the Combination Procedure of Correlated Errors, Jens Erler, Eur. Phys. J. C75 (2015) 453, arXiv:1507.08210.
Another Look at Confidence Intervals: Proposal for a More Relevant and Transparent Approach, Steven D. Biller, Scott M. Oser, Nucl.Instrum.Meth. A774 (2014) 103-119, arXiv:1405.5010.
Raster scan or 2-D approach?, Louis Lyons, arXiv:1404.7395, 2014.
MadMax, or Where Boosted Significances Come From, Tilman Plehn, Peter Schichtel, Daniel Wiegand, Phys. Rev. D89 (2014) 054002, arXiv:1311.2591.
Information and treatment of unknown correlations in the combination of measurements using the BLUE method, Andrea Valassi, Roberto Chierici, Eur. Phys. J. C74 (2014) 2717, arXiv:1307.4003.
A method for statistical comparison of histograms, Sergey Bityukov, Nikolai Krasnikov, Alexander Nikitenko, Vera Smirnova, arXiv:1302.2651, 2013.
Calculating error bars for neutrino mixing parameters, H. R. Burroughs, B. K. Cogswell, J. Escamilla-Roa, D. C. Latimer, D. J. Ernst, Phys. Rev. C85 (2012) 068501, arXiv:1204.1354.
Testing the approximations described in 'Asymptotic formulae for likelihood-based tests of new physics', Eric Burns, Wade Fisher, arXiv:1110.5002, 2011.
Cancelling out systematic uncertainties, Jorge Norena, Licia Verde, Raul Jimenez, Carlos Pena-Garay, Cesar Gomez, Mon. Not. Roy. Astron. Soc. 419 (2012) 1040, arXiv:1107.0729.
Power-Constrained Limits, Glen Cowan, Kyle Cranmer, Eilam Gross, Ofer Vitells, arXiv:1105.3166, 2011.
Some ways of combining optimum interval upper limits, S. Yellin, arXiv:1105.2928, 2011.
Asymptotic formulae for likelihood-based tests of new physics, Glen Cowan, Kyle Cranmer, Eilam Gross, Ofer Vitells, Eur. Phys. J. C71 (2011) 1554, arXiv:1007.1727.
How good are your fits? Unbinned multivariate goodness-of-fit tests in high energy physics, Mike Williams, JINST 5 (2010) P09004, arXiv:1006.3019.
Formalism for Simulation-based Optimization of Measurement Errors in High Energy Physics, Yuehong Xie, arXiv:0901.3305, 2009.
An Ad-Hoc Method for Obtaining $\chi^2$ Values from Unbinned Maximum Likelihood Fits, M. Williams, C. A. Meyer, arXiv:0807.0015, 2008.
Averaging Results with Theoretical Uncertainties, F. C. Porter, arXiv:0806.0530, 2008.
Use of the median in Physics and Astronomy, Jean-Michel Levy, arXiv:0804.0606, 2008.
Testing Consistency of Two Histograms, Frank C. Porter, arXiv:0804.0380, 2008.
On sensitivity calculations for neutrino oscillation experiments, Jan Conrad, Nucl. Instrum. Meth. A580 (2007) 1460-1465, arXiv:0710.2969.
Estimation of experimental data redundancy and related statistics, I. Grabec, arXiv:0704.0162, 2007.
Extraction of physical laws from joint experimental data, I. Grabec, arXiv:0704.0151, 2007.
Notes on statistical separation of classes of events, Giovanni Punzi, arXiv:physics/0611219, 2006.
Why your model parameter confidences might be too optimistic - unbiased estimation of the inverse covariance matrix, J. Hartlap, P. Simon, P. Schneider, Astron.Astrophys. (2006), arXiv:astro-ph/0608064.
A Test for the Presence of a Signal, Wolfgang A. Rolke, Angel M. Lopez, arXiv:physics/0606006, 2006.
Optimal Data-Based Binning for Histograms, Kevin H. Knuth, arXiv:physics/0605197, 2006.
A General Theory of Goodness of Fit in Likelihood Fits, Rajendran Raja, arXiv:physics/0509008, 2005.
On the Statistical Significance, Yongsheng Zhu, arXiv:physics/0507145, 2005.
Sifting data in the real world, Martin M. Block, Nucl. Instrum. Meth. A556 (2006) 308, arXiv:physics/0506010.
Simultaneous Least Squares Treatment of Statistical and Systematic Uncertainties, Werner M. Sun, Nucl. Instrum. Meth. A556 (2006) 325, arXiv:physics/0503050.
Late-Night Thoughts About the Significance of a Small Count of Nuclear or Particle Events, Ivan V. Anicin, arXiv:physics/0501108, 2005.
Inferring the success parameter p of a binomial model from small samples affected by background, G. D'Agostini, arXiv:physics/0412069, 2004.
Asymmetric Statistical Errors, Roger Barlow, arXiv:physics/0406120, 2004.
Computation of Confidence Levels for Exclusion or Discovery of a Signal with the Method of Fractional Event Counting, P.Bock, JHEP 01 (2007) 080, arXiv:hep-ex/0405072.
Asymmetric Uncertainties: Sources, Treatment and Potential Dangers, G. D'Agostini, arXiv:physics/0403086, 2004.
Facts, Values and Quanta, D. M. Appleby, arXiv:quant-ph/0402015, 2004.
Comments on Likelihood fits with variable resolution, Giovanni Punzi, eConf C030908 (2003) WELT002, arXiv:physics/0401045.
Peak finding through Scan Statistics, F. Terranova, Nucl. Instrum. Meth. A519 (2004) 659, arXiv:physics/0311020.
A note on the use of the word 'likelihood' in statistics and meteorology, Stephen Jewson, Anders Brix, Christine Ziehmann (ATLAS, CMS), Eur.Phys.J. C33 (2004) S924-S926, arXiv:physics/0310020.
Statistical Challenges with Massive Data Sets in Particle Physics, Bruce Knuteson, Paul Padley, arXiv:hep-ex/0305064, 2003.
Combining correlated measurements of several different physical quantities, A. Valassi, Nucl. Instrum. Meth. A500 (2003) 391-405.
Unbiased cut selection for optimal upper limits in neutrino detectors: the model rejection potential technique, Gary C. Hill, Katherine Rawlins, Astropart. Phys. 19 (2003) 393, arXiv:astro-ph/0209350.
Clustering statistics in cosmology, Vicent J. Martinez, Enn Saar, Proc.SPIE Int.Soc.Opt.Eng. (2002), arXiv:astro-ph/0209208.
Interpolation and smoothing, Marco Lombardi, Astron. Astrophys. 395 (2002) 733, arXiv:astro-ph/0208533.
Finding an upper limit in the presence of unknown background, S. Yellin, Phys. Rev. D66 (2002) 032005, arXiv:physics/0203002.
Analytic marginalization over CMB calibration and beam uncertainty, S. L. Bridle et al., Mon. Not. Roy. Astron. Soc. 335 (2002) 1193, arXiv:astro-ph/0112114.
Error estimates on parton density distributions, M. Botje, J. Phys. G28 (2002) 779-790, arXiv:hep-ph/0110123.
Frequentist and Bayesian confidence limits, Gunter Zech, Eur. Phys. J. direct C4 (2002) 12, arXiv:hep-ex/0106023.
Uncertainties of predictions from parton distribution functions. I: The Lagrange multiplier method, D. Stump et al., Phys. Rev. D65 (2001) 014012, arXiv:hep-ph/0101051.
Uncertainties of predictions from parton distribution functions. II: The Hessian method, J. Pumplin et al., Phys. Rev. D65 (2001) 014013, arXiv:hep-ph/0101032.
Confronting classical and Bayesian confidence limits to examples, Gunter Zech, arXiv:hep-ex/0004011, 2000.
Citations and the Zipf-Mandelbrot's law, Z. K. Silagadze, Complex Syst. 11 (1997) 487-499, arXiv:physics/9901035.
Expected coverage of Bayesian and classical intervals for a small number of events, O. Helene, Phys. Rev. D60 (1999) 037901.
Estimation of Asymmetry in Physics, S. Wilson, K. J. Coakley, Phys. Rev. E53 (1996) 2160-2168.
Why isn't every physicist a Bayesian?, R. D. Cousins, Am. J. Phys. 63 (1995) 398.
On the use of the covariance matrix to fit correlated data, G. D'Agostini, Nucl. Instrum. Meth. A346 (1994) 306-311.
How to Combine Correlated Estimates of a Single Physical Quantity, Louis Lyons, Duncan Gibaut, Peter Clifford, Nucl. Instrum. Meth. A270 (1988) 110.

8 - Articles - Talks

An exact framework for uncertainty quantification in Monte Carlo simulation, Paolo Saracco, Maria Grazia Pia, J. Phys. Conf. Ser. 513 (2014) 022033, arXiv:1311.5221. CHEP 2013.

9 - Applications

Publishing statistical models: Getting the most out of particle physics experiments, Kyle Cranmer et al., SciPost Phys. 12 (2022) 037, arXiv:2109.04981.
Effect of Systematic Uncertainty Estimation on the Muon $g-2$ Anomaly, Glen Cowan, EPJ Web Conf. 258 (2022) 09002, arXiv:2107.02652.
A methodology for theory uncertainties in the SMEFT, Michael Trott, Phys.Rev.D 104 (2021) 095023, arXiv:2106.13794.
An Error Analysis Toolkit for Binned Counting Experiments, B. Messerly et al. (MINERvA), EPJ Web Conf. 251 (2021) 03046, arXiv:2103.08677.
Statistical Methods for the Search of Sterile Neutrinos, Matteo Agostini, Birgit Neumair, Eur.Phys.J. C80 (2020) 750, arXiv:1906.11854.
Optimal prior for Bayesian inference in a constrained parameter space, Steen Hannestad, Thomas Tram, arXiv:1710.08899, 2017.
Is the bump significant? An axion-search example, Frederik Beaujean, Allen Caldwell, Olaf Reimann, Eur.Phys.J. C78 (2018) 793, arXiv:1710.06642.
A bump hunter's guide to model uncertainty, Mike Williams, JINST 12 (2017) P09034, arXiv:1705.03578.
A Poisson likelihood approach to fake lepton estimation with the matrix method, Erich W. Varnes, arXiv:1606.06817, 2016.
HistFitter software framework for statistical data analysis, M. Baak et al., Eur.Phys.J. C75 (2015) 153, arXiv:1410.1280.
Neutrino mass and Extreme Value Distributions in $\beta$-decay, J. G. Esteve, Fernando Falceto, J. Phys. G41 (2014) 055011, arXiv:1401.1644.
A comment on estimating sensitivity to neutrino mass hierarchy in neutrino experiments, Ofer Vitells, Alex Read, arXiv:1311.4076, 2013.
Quantifying the sensitivity of oscillation experiments to the neutrino mass ordering, Mattias Blennow, Pilar Coloma, Patrick Huber, Thomas Schwetz, JHEP 1403 (2014) 028, arXiv:1311.1822.
Discovering the Significance of 5 sigma, Louis Lyons, arXiv:1310.1284, 2013.
Confidence in a Neutrino Mass Hierarchy Determination, Emilio Ciuffoli, Jarah Evslin, Xinmin Zhang, JHEP 1401 (2014) 095, arXiv:1305.5150.
Statistical Evaluation of Experimental Determinations of Neutrino Mass Hierarchy, X. Qian et al., Phys. Rev. D86 (2012) 113011, arXiv:1210.3651.
Limit setting procedures and theoretical uncertainties in Higgs boson searches, Bernhard Mistlberger, Falko Dulat, arXiv:1204.3851, 2012.
Reinterpretion of Experimental Results with Basis Templates, Kanishka Rao, Daniel Whiteson, arXiv:1203.6642, 2012.
Estimating the significance of a signal in a multi-dimensional search, Ofer Vitells, Eilam Gross, Astropart. Phys. 35 (2011) 230-234, arXiv:1105.4355.
CHIWEI: A code of goodness of fit tests for weighted and unweighed histograms, Nikolai Gagunashvili, Comput.Phys.Commun. 183 (2012) 418-421, arXiv:1104.3733.
Statistical Significance of the Gallium Anomaly, Carlo Giunti, Marco Laveder, Phys. Rev. C83 (2011) 065504, arXiv:1006.3244.
On the Peirce's 'balancing reasons rule' failure in his 'large bag of beans' example, G. D'Agostini, arXiv:1003.3659, 2010.
On the so-called Boy or Girl Paradox, G. D'Agostini, J. Phys. Conf. Ser. 229 (2010) 012017, arXiv:1001.0708.
A Bayesian Assessment of P-Values for Significance Estimation of Power Spectra and an Alternative Procedure, with Application to Solar Neutrino Data, P.A. Sturrock, J.D. Scargle, Astrophys. J. 706 (2009) 393-398, arXiv:0904.1713.
A pitfall in the use of extended likelihood for fitting fractions of pure samples in mixed samples, A. Nappi, Comput. Phys. Commun. 180 (2009) 269, arXiv:0803.2711.
A practical guide to Basic Statistical Techniques for Data Analysis in Cosmology, Licia Verde, arXiv:0712.3028, 2007.
Forecasting neutrino masses from combining KATRIN and the CMB: Frequentist and Bayesian analyses, Ole Host, Ofer Lahav, Filipe B. Abdalla, Klaus Eitel, Phys. Rev. D76 (2007) 113005, arXiv:0709.1317.
On influence of experimental resolution on the statistical significance of a signal: implication for pentaquark searches, S.V. Chekanov, B.B. Levchenko, Phys. Rev. D76 (2007) 074025, arXiv:0707.2203.
Statistical methods applied to composition studies of ultrahigh energy cosmic rays, F. Catalani et al., Astropart. Phys. 28 (2007) 357-365, arXiv:astro-ph/0703582.
Information criteria for astrophysical model selection, Andrew R Liddle, Mon. Not. Roy. Astron. Soc. Lett. 377 (2007) L74-L78, arXiv:astro-ph/0701113.
A Likelihood Method for Measuring the Ultrahigh Energy Cosmic Ray Composition, HiRes (HiRes), Astropart. Phys. 26 (2006) 28-40, arXiv:astro-ph/0604558.
A procedure to produce excess, probability and significance maps and to compute point-sources flux upper limits, P. Billoir, A. Letessier-Selvon, arXiv:astro-ph/0507538, 2005.
Which cosmological model with dark energy - phantom or LambdaCDM, Wlodzimierz Godlowski Marek Szydlowski, Phys. Lett. B623 (2005) 10, arXiv:astro-ph/0507322.
Higher Criticism Statistic: Detecting and Identifying Non-Gaussianity in the WMAP First Year Data, L. Cayon, J. Jin, A. Treaster, Mon. Not. Roy. Astron. Soc. 362 (2005) 826, arXiv:astro-ph/0507246.
Blind search for the real sample: Application to the origin of ultra-high energy cosmic rays, Boris E. Stern, Juri Poutanen, Astrophys. J. 623 (2005) L33, arXiv:astro-ph/0501677.
Time series analysis in Astronomy: limits and potentialities, R. Vio, N. R. Kristensen, H. Madsen, W. Wamsteker, Astron.Astrophys. (2004), arXiv:astro-ph/0410367.
Nonparametric Inference for the Cosmic Microwave Background, Christopher R. Genovese et al., Statist.Sci. (2004), arXiv:astro-ph/0410140.
Probabilistic forecasts of temperature: measuring the utility of the ensemble spread, Stephen Jewson (STAR), J. Phys. G31 (2005) S179-S186, arXiv:physics/0410039.
Three-Point Statistics from a New Perspective, Istvan Szapudi, Astrophys. J. 605 (2004) L89, arXiv:astro-ph/0404476.
sPlot: a statistical tool to unfold data distributions, Muriel Pivk, Francois R. Le Diberder, Nucl. Instrum. Meth. A555 (2005) 356, arXiv:physics/0402083.
Neutrino spin and chiral dynamics in gravitational fields, Dinesh Singh, Phys. Rev. D71 (2005) 105003, arXiv:gr-qc/0401044.
Do probabilistic medium-range temperature forecasts need to allow for non-normality?, Stephen Jewson, arXiv:physics/0310060, 2003.
Comparing the ensemble mean and the ensemble standard deviation as inputs for probabilistic medium-range temperature forecasts, Stephen Jewson, arXiv:physics/0310059, 2003.
The statistical distribution of money and the rate of money transference, Juan C. Ferrero, arXiv:cond-mat/0306322, 2003.
Tests of Statistical Significance and Background Estimation in Gamma Ray Air Shower Experiments, R. Fleysher et al., Astrophys. J. 603 (2004) 355, arXiv:astro-ph/0306015.
Maximum likelihood analysis of the first KamLAND results, A. Ianni, J. Phys. G29 (2003) 2107, arXiv:hep-ph/0302230.
Improved approximations of Poissonian errors for high confidence levels, Harald Ebeling, Mon. Not. Roy. Astron. Soc. 349 (2004) 768, arXiv:astro-ph/0301285.
Higgs Statistics for Pedestrians, Eilam Gross, Amit Klier, arXiv:hep-ex/0211058, 2002.
On the Determination of the $B$ Lifetime by Combining the Results of Different Experiments, Louis Lyons, Alexander J. Martin, David H. Saxon, Phys. Rev. D41 (1990) 982.

10 - Applications - Talks

Confidence in the neutrino mass hierarchy, Jarah Evslin, arXiv:1310.4007, 2013. NUFACT 2013.
Probably a discovery: Bad mathematics means rough scientific communication, G. D'Agostini, arXiv:1112.3620, 2011. University of Perugia, 15-16 April 2011 and at MAPSES School in Lecce, 23-25 November 2011.
Discovery and Upper Limits in Search for Exotic Physics with Neutrino Telescopes, Jan Conrad, arXiv:astro-ph/0612082, 2006. Workshop on Exotic Physics with Neutrino Telescope, Uppsala, Sweden, Sept. 2006.
Maximum likelihood estimation for a group of physical transformations, G. Chiribella, G. M. D'Ariano, P. Perinotti, M. F. Sacchi, arXiv:quant-ph/0507007, 2005. Workshop on "Quantum entanglement in physical and information sciences", Pisa, December 14-18, 2004.
Statistical Challenges of Cosmic Microwave Background Analysis, Benjamin D. Wandelt, ECONF C030908 (2003) THAT004, arXiv:astro-ph/0401622. PHYSTAT2003, SLAC, Stanford, Ca, USA, 8-11 Sep 2003.
Setting confidence intervals in coincidence search analysis, Lucio Baggio, Giovanni A. Prodi, ECONF C030908 (2003) WELT003, arXiv:astro-ph/0312353. PHYSTAT2003, SLAC, Stanford, Ca, USA, 8-11 Sep 2003.
Blind Analysis in Particle Physics, Aaron Roodman, ECONF C030908 (2003) TUIT001, arXiv:physics/0312102. PHYSTAT2003, SLAC, Stanford, Ca, USA, 8-11 Sep 2003.
Measures of Significance in HEP and Astrophysics, J. T. Linnemann, ECONF C030908 (2003) MOBT001, arXiv:physics/0312059. PHYSTAT2003, SLAC, Stanford, Ca, USA, 8-11 Sep 2003.
Statistical Issues in Particle Physics - A View from BaBar, Frank C. Porter et al. (BaBar), ECONF C030908 (2003) WEAT002, arXiv:physics/0311092. PHYSTAT2003, SLAC, Stanford, Ca, USA, 8-11 Sep 2003.

11 - Quantum Probability

Quantum Mechanics as Quantum Information (and only a little more), Christopher A. Fuchs, arXiv:quant-ph/0205039, 2002.
Unknown Quantum States: the Quantum de Finetti Representation, Carlton M. Caves, Christopher A. Fuchs, Rudiger Schack, J. Math. Phys. 43 (2002) 4537.
Quantum Probabilities as Bayesian Probabilities, Carlton M. Caves, Christopher A. Fuchs, Rudiger Schack, Phys. Rev. A65 (2002) 022305.
Conditions for compatibility of quantum-state assignments, Carlton M. Caves, Christopher A. Fuchs, Rudiger Schack, Phys. Rev. A66 (2002) 062111.
Notes on a Paulian Idea: Foundational, Historical, Anecdotal and Forward-Looking Thoughts on the Quantum, Christopher A. Fuchs, arXiv:quant-ph/0105039, 2001.
Quantum Probability from Decision Theory?, H. Barnum, C. M. Caves, J. Finkelstein, C. A. Fuchs, R. Schack, Proc. Roy. Soc. Lond. A456 (2000) 1175-1182, arXiv:quant-ph/9907024.
Quantum Theory of Probability and Decisions, David Deutsch, Proc.Roy.Soc.Lond. A455 (1999) 3129, arXiv:quant-ph/9906015.
The Statistical Interpretation of Quantum Mechanics, L. E. Ballentine, Rev. Mod. Phys. 42 (1970) 358.

12 - Bayesian Theory

Metric Gaussian Variational Inference, Jakob Knollmuller, Torsten A. Enslin, arXiv:1901.11033, 2019.
Bayesian parameter estimation of miss-specified models, Johannes Oberpriller, T. A. Enslin, arXiv:1812.08194, 2018.
The Bayesian Who Knew Too Much, Yann Benetreau-Dupin, arXiv:1412.8488, 2014.
Bayesian Reweighting for Global Fits, Nobuo Sato, J. F. Owens, Harrison Prosper, Phys. Rev. D89 (2014) 114020, arXiv:1310.1089.
Computing the Bayesian Evidence from a Markov Chain Monte Carlo Simulation of the Posterior Distribution, Martin D. Weinberg, arXiv:0911.1777, 2009.
A Quantitative Occam's Razor, R. D. Sorkin, Int. J. Theor. Phys. 22 (1983) 1091, arXiv:astro-ph/0511780.
A Hidden Markov model for Bayesian data fusion of multivariate signals, O. Feron, A. Mohammad-Djafari, Phys.Lett. A337 (2005) 17-21, arXiv:physics/0403149. presented at Fifth Int. Triennial Calcutta Symposium on Probability and Statistics, 28-31 December. 2003, Dept. of Statistics, Calcutta University, Kolkata, India.
A Bayesian approach to change point analysis of discrete time series, A. Mohammad-Djafari, O. Feron, arXiv:physics/0403148, 2004.
Probabilities as Measures of Information, F. G. Perey, arXiv:quant-ph/0310073, 2003.
A Good Measure for Bayesian Inference, H. L. Harney, Astropart.Phys. 17 (2002) 441-458, arXiv:physics/0103030.
Inferring the intensity of Poisson processes at the limit of the detector sensitivity (with a case study on gravitational wave burst search), P. Astone, G. D. D'Agostini, Ann.Phys. (1999), arXiv:hep-ex/9909047.
Bayesian analysis of multi-source data, P. C. Bhat, H. B. Prosper, S. S. Snyder, Phys. Lett. B407 (1997) 73-78.
A Multidimensional unfolding method based on Bayes' theorem, G. D. D'Agostini, Nucl. Instrum. Meth. A362 (1995) 487-498.
Small signal analysis in high-energy physics: a bayesian approach, H. B. Prosper, Phys. Rev. D37 (1988) 1153-1160.
Reply to 'comment on 'small signal analysis in high-energy physics: a bayesian approach'.', H. B. Prosper, Phys. Rev. D38 (1988) 3584-3585.
Experimental signs pointing to a bayesian instead of a classical approach for experiments with a small number of events, B. Escoubes, S. De Unamuno, O. Helene, Nucl. Instrum. Meth. A257 (1987) 346-360.
A bayesian analysis of experiments with small numbers of events, H. B. Prosper, Nucl. Instrum. Meth. A241 (1985) 236-240.
An Essay towards solving a Problem in the Doctrine of Chances. By the late Rev. Mr. Bayes, F. R. S. communicated by Mr. Price, in a letter to John Canton, M. A. and F. R. S., Thomas Bayes, Philosophical Transactions of the Royal Society 53 (1763) 370-418.

13 - Bayesian Theory - Talks

Wrong Priors, Carlos C. Rodriguez, Astrophys.J. 673 (2008) 1067, arXiv:0709.1067. MaxEnt2007.
Bayesian reference analysis, L. Demortier, 2005. PHYSTATO5: Statistical Problems in Particle Physics, Astrophysics and Cosmology, Oxford, England, United Kingdom, 12-15 Sep 2005.
From Observations to Hypotheses: Probabilistic Reasoning Versus Falsificationism and its Statistical Variations, G. D'Agostini, arXiv:physics/0412148, 2004. 2004 Vulcano Workshop on Frontier Objects in Astrophysics and Particle Physics, Vulcano (Italy) May 24-29, 2004.
Entropic Priors, A. Caticha, R. Preuss, Phys. Rev. D69 (2004) 117301, arXiv:physics/0312131. MaxEnt'03, the 23d International Workshop on Bayesian Inference and Maximum Entropy Methods (August 3-8, 2003, Jackson Hole, WY, USA).
Relative Entropy and Inductive Inference, A. Caticha, Phys. Rev. D69 (2004) 085010, arXiv:physics/0311093. MaxEnt23, the 23rd International Workshop on Bayesian Inference and Maximum Entropy Methods (August 3-8, 2003, Jackson Hole, WY, USA).
Why be a Bayesian?, M. Goldstein, 2002. Advanced Statistical Techniques in Particle Physics, Durham, UK, 18-22 March 2002.
Objectively derived default 'prior' depends on stopping rule; Bayesian treatment of nuisance parameters is defended, G. Kahrimanis, 2002. Advanced Statistical Techniques in Particle Physics, Durham, UK, 18-22 March 2002.
Bayesian reasoning versus conventional statistics in high energy physics, G. D'Agostini, arXiv:physics/9811046, 1998. 18th International Workshop on Maximum Entropy and Bayesian Methods (MaxEnt 98), Garching, Germany, 27-31 Jul 1998.
Unfolding of experimental distributions by an interactive use of Bayes's formula with intermediated smoothing, G. D'Agostini, 1995. In 'Pisa 1995, New computing techniques in physics research', 697-702.

14 - Bayesian Theory - Applications

Exploring phase space with Nested Sampling, David Yallup, Timo Jansen, Steffen Schumann, Will Handley, Eur.Phys.J.C 82 (2022) 8, arXiv:2205.02030.
Least-Informative Priors for $0\nu\beta\beta$ Decay Searches, Frank F. Deppisch, Graham Van Goffrier, Phys.Rev.D 104 (2021) 055040, arXiv:2103.06660.
BAT.jl - A Julia-based tool for Bayesian inference, Oliver Schulz, Frederik Beaujean, Allen Caldwell, Cornelius Grunwald, Vasyl Hafych, Kevin Kroninger, Salvatore La Cagnina, Lars Rohrig, Lolian Shtembari, arXiv:2008.03132, 2020.
Data Analysis Recipes: Products of multivariate Gaussians in Bayesian inferences, David W. Hogg, Adrian M. Price-Whelan, Boris Leistedt, arXiv:2005.14199, 2020.
Constraining power of open likelihoods, made prior-independent, S. Gariazzo, Eur.Phys.J. C80 (2020) 552, arXiv:1910.06646.
Cosmology-marginalized approaches in Bayesian model comparison: the neutrino mass as a case study, S. Gariazzo, O. Mena, Phys.Rev. D99 (2019) 021301, arXiv:1812.05449.
Maximum entropy priors with derived parameters in a specified distribution, Will Handley, Marius Millea, Entropy 21 (2019) 272, arXiv:1804.08143.
Bayesian Methods for Exoplanet Science, Hannu Parviainen, arXiv:1711.03329, 2017.
Bayesian global analysis of neutrino oscillation data, Johannes Bergstrom, M. C. Gonzalez-Garcia, Michele Maltoni, Thomas Schwetz, JHEP 09 (2015) 200, arXiv:1507.04366.
Objective Bayesian analysis of counting experiments with correlated sources of background, Diego Casadei, Kevin Kroninger, arXiv:1504.02566, 2015.
Bayesian Model comparison of Higgs couplings, Johannes Bergstrom, Stella Riad, Phys. Rev. D91 (2015) 075008, arXiv:1411.4876.
The Proton Radius from Bayesian Inference, Krzysztof M. Graczyk, Cezary Juszczak, Phys. Rev. C90 (2014) 054334, arXiv:1408.0150.
Bayesian parameter estimation of core collapse supernovae using gravitational wave simulations, Matthew C. Edwards, Renate Meyer, Nelson Christensen, Inverse Prob. 30 (2014) 114008, arXiv:1407.7549.
On the Bayesian approach to neutrino mass ordering, Mattias Blennow, JHEP 1401 (2014) 139, arXiv:1311.3183.
Bayesian analysis of multiple direct detection experiments, Chiara Arina, Phys.Dark Univ. 5-6 (2014) 1-17, arXiv:1310.5718.
Bayesian Approach for a Neutrino Point Source Analysis, Debanjan Bose, Lionel Brayeur, Martin Casier, Geraldina Golup, Nick van Eijndhoven, Astroparticle Physics 50-52C (2013) , pp. 57-64, arXiv:1212.2008.
A Bayesian technique for improving the sensitivity of the atmospheric neutrino L/E analysis, Andrew Blake, John Chapman, Mark Thomson, Nucl.Instrum.Meth. A707 (2013) 127-134, arXiv:1208.2899.
Quantified naturalness from Bayesian statistics, Sylvain Fichet, Phys. Rev. D86 (2012) 125029, arXiv:1204.4940.
Bayesian Implications of Current LHC and XENON100 Search Limits for the Constrained MSSM, Andrew Fowlie, Artur Kalinowski, Malgorzata Kazana, Leszek Roszkowski, Y.-L. Sming Tsai, Phys. Rev. D85 (2012) 075012, arXiv:1111.6098.
How to Use Experimental Data to Compute the Probability of Your Theory, Georgios Choudalakis, arXiv:1110.5295, 2011.
Signal + background model in counting experiments, Diego Casadei, JINST 7 (2012) P01012, arXiv:1108.4270.
Bayesian analysis of the astrobiological implications of life's early emergence on Earth, David S. Spiegel, Edwin L. Turner, Proc.Nat.Acad.Sci. 109 (2011) 395-400, arXiv:1107.3835.
Two-Photon Exchange Effect Studied with Neural Networks, Krzysztof M. Graczyk, Phys. Rev. C84 (2011) 034314, arXiv:1106.1204.
A Bayesian view of the current status of dark matter direct searches, Chiara Arina, Jan Hamann, Yvonne Y. Y. Wong, JCAP 1109 (2011) 022, arXiv:1105.5121.
A Bayesian approach to evaluate confidence intervals in counting experiments with background, F. Loparco, M. N. Mazziotta, Nucl. Instrum. Meth. A646 (2011) 167-173, arXiv:1105.3041.
Z' Bosons at Colliders: a Bayesian Viewpoint, Jens Erler, Paul Langacker, Shoaib Munir, Eduardo Rojas, JHEP 11 (2011) 076, arXiv:1103.2659.
Bayesian Inference in the Scaling Analysis of Critical Phenomena, Kenji Harada, arXiv:1102.4149, 2011.
False-alarm probability in relation to over-sampled power spectra, with application to Super-Kamiokande solar neutrino data, Peter A. Sturrock, Jeffrey D. Scargle, Astrophys. J. 718 (2010) 527-529, arXiv:1006.0546.
A Bayesian approach to QCD sum rules, Philipp Gubler, Makoto Oka, Prog. Theor. Phys. 124 (2010) 995-1018, arXiv:1005.2459.
Direct determination of the solar neutrino fluxes from solar neutrino data, M.C. Gonzalez-Garcia, Michele Maltoni, Jordi Salvado, JHEP 05 (2010) 072, arXiv:0910.4584.
A Bayesian Analysis of the Constrained NMSSM, Daniel E. Lopez-Fogliani, Leszek Roszkowski, Roberto Ruiz de Austri, Tom A. Varley, Phys. Rev. D80 (2009) 095013, arXiv:0906.4911.
Improving Application of Bayesian Neural Networks to Discriminate Neutrino Events from Backgrounds in Reactor Neutrino Experiments, Ye Xu, WeiWei Xu, YiXiong Meng, Bin Wu, JINST 4 (2009) P01004, arXiv:0901.1497.
Bayesian optimal reconstruction of the primordial power spectrum, M. Bridges, F. Feroz, M.P. Hobson, A.N. Lasenby, Mon.Not.Roy.Astron.Soc. 400 (2009) 1075-1084, arXiv:0812.3541.
Applying Bayesian Neural Network to Determine Neutrino Incoming Direction in Reactor Neutrino Experiments and Supernova Explosion Location by Scintillator Detectors, Weiwei Xu, Ye Xu, Yixiong Meng, Bin Wu, JINST 4 (2009) P01002, arXiv:0812.2713.
Bayesian approach and Naturalness in MSSM analyses for the LHC, M. E. Cabrera, J. A. Casas, R. Ruiz de Austri, JHEP 03 (2009) 075, arXiv:0812.0536.
Bayesian Constraints on $\vartheta_{13}$ from Solar and KamLAND Neutrino Data, H.L. Ge, C. Giunti, Q.Y. Liu, Phys. Rev. D80 (2009) 053009, arXiv:0810.5443.
MultiNest: an efficient and robust Bayesian inference tool for cosmology and particle physics, F. Feroz, M. P. Hobson, M. Bridges, Mon.Not.Roy.Astron.Soc. 398 (2009) 1601-1614, arXiv:0809.3437.
Applications of Bayesian Probability Theory in Astrophysics, Brendon J. Brewer, arXiv:0809.0939, 2008.
A Bayesian approach to power-spectrum significance estimation, with application to solar neutrino data, P. A. Sturrock, arXiv:0809.0276, 2008.
Bayesian Methods for Parameter Estimation in Effective Field Theories, Matthias R. Schindler, Daniel R. Phillips, Annals Phys. 324 (2009) 682-708, arXiv:0808.3643.
Bayesian analysis of sparse anisotropic universe models and application to the 5-yr WMAP data, Nicolaas E. Groeneboom, Hans Kristian Eriksen, Astrophys. J. 690 (2009) 1807-1819, arXiv:0807.2242.
Bayesian Analysis of Solar Oscillations, M. S. Marsh, J. Ireland, T. Kucera, Astrophys. J. 681 (2008) 672-679, arXiv:0804.1447.
Limitations of Bayesian Evidence Applied to Cosmology, G. Efstathiou, Mon.Not.Roy.Astron.Soc. 388 (2008) 1314, arXiv:0802.3185.
Applying Bayesian Neural Networks to Event Reconstruction in Reactor Neutrino Experiments, Ye Xu, Weiwei Xu, Yixiong Meng, Kaien Zhu, Wei Xu, Nucl. Instrum. Meth. A592 (2008) 451-455, arXiv:0712.4042.
A Bayesian analysis of pentaquark signals from CLAS data, D. G. Ireland et al. (CLAS), Phys. Rev. Lett. 100 (2008) 052001, arXiv:0709.3154.
Bayesian analysis of the low-resolution polarized 3-year WMAP sky maps, H. K. Eriksen et al., Astrophys. J. 665 (2007) L1, arXiv:0705.3643.
Bayesian reconstruction of the cosmological large-scale structure: methodology, inverse algorithms and numerical optimization, F.S. Kitaura, T.A. Ensslin, Mon.Not.Roy.Astron.Soc. 389 (2008) 497, arXiv:0705.0429.
Are We Typical?, James B. Hartle, Mark Srednicki, Phys. Rev. D75 (2007) 123523, arXiv:0704.2630.
Bayesian Estimation Applied to Multiple Species: Towards cosmology with a million supernovae, Martin Kunz, Bruce A. Bassett, Renee Hlozek, Phys. Rev. D75 (2007) 103508, arXiv:astro-ph/0611004.
The Zurich Extragalactic Bayesian Redshift Analyzer (ZEBRA) and its first application: COSMOS, R. Feldmann et al., Mon. Not. Roy. Astron. Soc. 372 (2006) 565-577, arXiv:astro-ph/0609044.
Bayesian Inference from Observations of Solar-Like Oscillations, Brendon J. Brewer, Timothy R. Bedding, Hans Kjeldsen, Dennis Stello, Astrophys. J. 654 (2006) 551-557, arXiv:astro-ph/0608571.
Bayesian Statistics at Work: the Troublesome Extraction of the CKM Phase alpha, J. Charles et al., arXiv:hep-ph/0607246, 2006.
Testing the stability of the solar neutrino LMA solution with a Bayesian analysis, B. L. Chen, H. L. Ge, C. Giunti, Q. Y. Liu, Mod. Phys. Lett. A21 (2006) 2269-2282, arXiv:hep-ph/0605195.
A Bayesian model selection analysis of WMAP3, David Parkinson, Pia Mukherjee, Andrew R Liddle, Phys. Rev. D73 (2006) 123523, arXiv:astro-ph/0605003.
Bayesian analysis of Friedmannless cosmologies, Oystein Elgaroy, Tuomas Multamaki, JCAP 0609 (2006) 002, arXiv:astro-ph/0603053.
Relationalism vs. Bayesianism, Thomas Marlow, arXiv:gr-qc/0603015, 2006.
Bayesian methods of astronomical source extraction, Richard S. Savage Seb Oliver, Astrophys. J. 661 (2007) 1339-1346, arXiv:astro-ph/0512597.
A Bayesian analysis of the primordial power spectrum, M. Bridges, A.N. Lasenby, M.P. Hobson, Mon. Not. Roy. Astron. Soc. 369 (2006) 1123-1130, arXiv:astro-ph/0511573.
Bayesian estimation of pulsar parameters from gravitational wave data, Réjean J. Dupuis, Graham Woan, Phys. Rev. D72 (2005) 102002, arXiv:gr-qc/0508096.
Cosmography, Decelerating Past, and Cosmological Models: Learning the Bayesian Way, Moncy V. John, Astrophys. J. 630 (2005) 667, arXiv:astro-ph/0506284.
Applications of Bayesian Model Selection to Cosmological Parameters, Roberto Trotta, Mon. Not. Roy. Astron. Soc. 378 (2007) 72-82, arXiv:astro-ph/0504022.
Bayesian model selection and isocurvature perturbations, Maria Beltran et al., Phys. Rev. D71 (2005) 063532, arXiv:astro-ph/0501477.
A Bayesian Estimator for Linear Calibration Error Effects in Thermal Remote Sensing, J. A. Morgan, J. Phys. A38 (2005) 7313-7324, arXiv:physics/0501087.
Bayesian evidence as a tool for comparing datasets, Phil Marshall, Nutan Rajguru, Anze Slosar, Phys. Rev. D73 (2006) 067302, arXiv:astro-ph/0412535.
Bayesian Adaptive Exploration, Thomas J. Loredo, AIP Conf.Proc.707:330-346 (2003), arXiv:astro-ph/0409386.
Interval estimation in the presence of nuisance parameters. 1. Bayesian approach, Joel Heinrich et al., arXiv:physics/0409129, 2004.
Bayesian Power Spectrum Analysis of the First-Year WMAP data, I.J. O'Dwyer et al., Astrophys. J. 617 (2004) L99, arXiv:astro-ph/0407027.
Observational constraints on cosmic strings: Bayesian analysis in a three dimensional parameter space, Levon Pogosian, Mark Wyman, Ira Wasserman, JCAP 09 (2004) 008, arXiv:astro-ph/0403268.
A hidden Markov Model for image fusion and their joint segmentation in medical image computing, Olivier Feron, Ali Mohammad-Djafari, arXiv:physics/0403150, 2004.
Bayesian Estimation for Land Surface Temperature Retrieval: The Nuisance of Emissivities, John A. Morgan, arXiv:physics/0402099, 2004.
A Neural Bayesian Estimator for Conditional Probability Densities, Michael Feindt, arXiv:physics/0402093, 2004.
Revealing the Nature of Dark Energy Using Bayesian Evidence, T. D. Saini, J. Weller, S. L. Bridle, Mon. Not. Roy. Astron. Soc. 348 (2004) 603, arXiv:astro-ph/0305526.
Bayesian model comparison applied to the Explorer-Nautilus 2001 coincidence data, P. Astone, G. D'Agostini, S. D'Antonio, Class. Quant. Grav. 20 (2003) S769-S784, arXiv:gr-qc/0304096.
A Bayesian Analysis of the Cepheid Distance Scale, T. G. Barnes III et al., Astrophys. J. 592 (2003) 539, arXiv:astro-ph/0303656.
Model independent information on solar neutrino oscillations, M. V. Garzelli, C. Giunti, Phys. Rev. D65 (2002) 093005, arXiv:hep-ph/0111254.
Bayesian view of solar neutrino oscillations, M. V. Garzelli, C. Giunti, JHEP 12 (2001) 017, arXiv:hep-ph/0108191.
Bayesian analysis of neutrinos observed from supernova SN 1987A, Thomas J. Loredo, Don Q. Lamb, Phys. Rev. D65 (2002) 063002, arXiv:astro-ph/0107260.
2000 CKM-triangle analysis: A critical review with updated experimental inputs and theoretical parameters, Marco Ciuchini et al., JHEP 07 (2001) 013, arXiv:hep-ph/0012308.
Constraints on the Higgs boson mass from direct searches and precision measurements, G. D'Agostini, G. Degrassi, Eur. Phys. J. C10 (1999) 663-675, arXiv:hep-ph/9902226.
Inferring the Spatial and Energy Distribution of Gamma-Ray Burst Sources. III. Anisotropic Models, T. J. Loredo, I. M. Wasserman, Astrophys. J. 502 (1998) 108.

15 - Bayesian Theory - Applications - Talks

Robust posterior inference when statistically emulating forward simulations, Grigor Aslanyan, Richard Easther, Nathan Musoke, Layne C. Price, arXiv:2004.11929, 2020. ICLR 2020.
Fully Bayesian Unfolding with Regularization, Petr Baron, arXiv:2001.05877, 2020. 12th International Workshop on Top Quark Physics proceedings.
Bayesian component separation: The Planck experience, Ingunn Kathrine Wehus, Hans Kristian Eriksen, IAU Symp. 333 (2017) 274-279, arXiv:1712.10223. IAUS333 Peering towards Cosmic Dawn.
The NIFTY way of Bayesian signal inference, Marco Selig, arXiv:1412.7160, 2014. 33rd International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering (MaxEnt 2013).
Model Inference with Reference Priors, Maurizio Pierini, Harrison Prosper, Sezen Sekmen, Maria Spiropulu, arXiv:1107.2877, 2011. PHYSTAT11.
Was There a Decelerating Past for the Universe?, Moncy V. John, Aip Conf. Proc. 822 (2006) 34, arXiv:astro-ph/0509509. 1st Crisis in Cosmology Conference (CCC-1), June 23-25, 2005 at Moncao, Portugal.
Accounting for Source Uncertainties in Analyses of Astronomical Survey Data, Thomas J. Loredo, Aip Conf. Proc. 735 (2005) 195, arXiv:astro-ph/0409387. Bayesian Inference And Maximum Entropy Methods In Science And Engineering: 24th International Workshop, Garching, Germany, 2004.
MAGIC: Exact Bayesian Covariance Estimation and Signal Reconstruction for Gaussian Random Fields, Benjamin D. Wandelt, ECONF C030908 (2003) WELT001, arXiv:astro-ph/0401623. PHYSTAT2003, SLAC, Stanford, Ca, USA, 8-11 Sep 2003.
Bayesian Wavelet Based Signal and Image Separation, M. M. Ichir, A. Mohammad-Djafari, arXiv:physics/0311033, 2003. Int. Conf. on Bayesian Inference and Maximum Entropy Methods (Maxent 2003) Jackson Hole (WY), USA.
Localization of GRBs by Bayesian Analysis of Data from the HETE WXM, C. Graziani, D. Q. Lamb et al. (HETE Science Team), AIP Conf.Proc. (2002), arXiv:astro-ph/0210429. AIP proc. 'Gamma-Ray Burst and Afterglow Astronomy 2001' Woods Hole, Massachusetts.

16 - Frequentist Statistics

Searching for new physics with profile likelihoods: Wilks and beyond, Sara Algeri, Jelle Aalbers, Knut Dundas Mora, Jan Conrad, arXiv:1911.10237, 2019.
Fitting theory to data in the presence of background uncertainties, Byron Roe, arXiv:1408.7075, 2014.
Confidence intervals with a priori parameter bounds, A. V. Lokhov, F. V. Tkachov, Phys. Part. Nucl. 46 (2015) 347-365, arXiv:1403.5429. [Fiz. Elem. Chast. Atom. Yadra46,no.3.(2015)].
The Profile likelihood ratio and the look elsewhere effect in high energy physics, Gioacchino Ranucci, Nucl. Instrum. Meth. A661 (2012) 77-85, arXiv:1201.4604.
Negatively Biased Relevant Subsets Induced by the Most-Powerful One-Sided Upper Confidence Limits for a Bounded Physical Parameter, Robert D. Cousins, IEEE Trans.Nucl.Sci. 57 (2011) 754-759, arXiv:1109.2023.
Trial factors for the look elsewhere effect in high energy physics, Eilam Gross, Ofer Vitells, Eur. Phys. J. C70 (2010) 525-530, arXiv:1005.1891.
Comments on the Unified approach to the construction of Classical confidence intervals, W. Wittek, H. Bartko, T. Schweizer, arXiv:0706.3622, 2007.
Is unbiasing estimators always justified ?, Jean-Michel Levy, arXiv:hep-ph/0604133, 2006.
Transcending The Least Squares, Fyodor V. Tkachov, arXiv:physics/0604127, 2006.
Neyman and Feldman-Cousins intervals for a simple problem with an unphysical region, and an analytic solution, B.D. Yabsley, arXiv:hep-ex/0604055, 2006.
Goodness-of-fit tests in many dimensions, A. van Hameren, Nucl. Instrum. Meth. A559 (2006) 167, arXiv:physics/0405008.
Confidence Intervals with Frequentist Treatment of Statistical and Systematic Uncertainties, Wolfgang A. Rolke, Angel M. Lopez, Jan Conrad, Nucl. Instrum. Meth. A551 (2005) 493, arXiv:physics/0403059.
Inference for bounded parameters, D. A. S. Fraser, N. Reid, A. C. M. Wong, Phys. Rev. D69 (2004) 033002.
The uniformly most powerful test of statistical significance for counting-type experiments with background, L. Fleysher et al., Phys. Rev.D (2003), arXiv:physics/0306146.
Asymmetric Systematic Errors, Roger Barlow, arXiv:physics/0306138, 2003.
Testing the statistical compatibility of independent data sets, M. Maltoni, T. Schwetz, Phys. Rev. D68 (2003) 033020, arXiv:hep-ph/0304176.
Cross-Correlation and Maximum Likelihood Analysis: A New Approach to Combine Cross-Correlation Functions, Shay Zucker, Mon. Not. Roy. Astron. Soc. 342 (2003) 1291, arXiv:astro-ph/0303426.
A new class of binning free, multivariate goodness-of-fit tests: the energy tests, B. Aslan, G. Zech, arXiv:hep-ex/0203010, 2002.
A calculator for confidence intervals, Roger Barlow, Comput. Phys. Commun. 149 (2002) 97-102, arXiv:hep-ex/0203002.
Including systematic uncertainties in confidence interval construction for Poisson statistics, J. Conrad, O. Botner, A. Hallgren, Carlos Perez de los Heros, Phys. Rev. D67 (2003) 012002, arXiv:hep-ex/0202013.
The power of confidence intervals, Carlo Giunti, Marco Laveder, Nucl. Instrum. Meth. A480 (2002) 763, arXiv:hep-ex/0011069.
What is the usefulness of Frequentist confidence intervals?, Carlo Giunti, arXiv:hep-ex/0003001, 2000.
The physical significance of confidence intervals, Carlo Giunti, Marco Laveder, Mod. Phys. Lett. 12 (2001) 1155-1168, arXiv:hep-ex/0002020.
Coverage of confidence intervals based on conditional probability, M. Mandelkern, J. Schultz, JHEP 11 (2000) 036.
The statistical analysis of Gaussian and Poisson signals near physical boundaries, Mark Mandelkern, Jonas Schultz, J. Math. Phys. 41 (2000) 5701-5709, arXiv:hep-ex/9910041.
Treatment of the background error in the statistical analysis of Poisson processes, C. Giunti, Phys. Rev. D59 (1999) 113009, arXiv:hep-ex/9901015.
A new ordering principle for the classical statistical analysis of Poisson processes with background, C. Giunti, Phys. Rev. D59 (1999) 053001, arXiv:hep-ph/9808240.
Small signal with background: Objective confidence intervals and regions for physical parameters from the principle of maximum likelihood, S. Ciampolillo, Nuovo Cim. A111 (1998) 1415-1430.
A Unified approach to the classical statistical analysis of small signals, Gary J. Feldman, Robert D. Cousins, Phys. Rev. D57 (1998) 3873-3889, arXiv:physics/9711021.
Impacts of Data Transformations on Least-Squares Solutions and Their Significance in Data Analysis and Evaluation, Satoshi Chiba, Donald L. Smith, Journal of Nuclear Science and Technology 31 (1994) 770-781.
A Method which eliminates the discreteness in Poisson confidence limits and lessens the effect of moving cuts specifically to eliminate candidate events, Robert Cousins, Nucl. Instrum. Meth. A337 (1994) 557-565.
Incorporating systematic uncertainties into an upper limit, Robert D. Cousins, Virgil L. Highland, Nucl. Instrum. Meth. A320 (1992) 331-335.
Statistical notes on the problem of experimental observations near an unphysical region, F. James, M. Roos, Phys. Rev. D44 (1991) 299-301.
Upper limits in experiments with background or measurement errors, G. Zech, Nucl. Instrum. Meth. A277 (1989) 608.
Clarification of the use of chi square and likelihood functions in fits to histograms, Steve Baker, Robert D. Cousins, Nucl. Instrum. Meth. 221 (1984) 437-442.
Interpretation of the shape of the likelihood function around its minimum, F. James, Comput. Phys. Commun. 20 (1980) 29-35.
Errors on ratios of small numbers of events, Frederick James, Matts Roos, Nucl. Phys. B172 (1980) 475.
A Monte Carlo method for the analysis of low statistics experiments, G. Zech, Nucl. Instr. Meth. 157 (1978) 551.
'MINUIT' a system for function minimization and analysis of the parameter errors and correlations, F. James, M. Roos, Comput. Phys. Commun. 10 (1975) 343-367.
The Large-Sample Distribution of the Likelihood Ratio for Testing Composite Hypotheses, S. S. Wilks, Annals Math. Statist. 9 (1938) 60-62.
Powerful goodness-of-fit tests based on the likelihood ratio, Jin Zhang, Journal of the Royal Statistical Society Series B (64) 281-294.

17 - Frequentist Statistics - Talks

About the proof of the so called exact classical confidence intervals. Where is the trick?, G. D'Agostini, arXiv:physics/0605140, 2006. Lectures to graduate students at the University of Rome 'La Sapienza'.
Ordering Algorithms and Confidence Intervals in the Presence of Nuisance Parameters, Giovanni Punzi, arXiv:physics/0511202, 2005. PhyStat2005, Oxford, UK, Sept 2005.
Constructing Ensembles of Pseudo-Experiments, Luc Demortier, ECONF C030908 (2003) WEMT003, arXiv:physics/0312100. PHYSTAT2003, SLAC, Stanford, Ca, USA, 8-11 Sep 2003.
An Unbinned Goodness-of-Fit Test Based on the Random Walk, K. Kinoshita, ECONF C030908 (2003) MOCT002, arXiv:physics/0312014. PHYSTAT2003, SLAC, Stanford, Ca, USA, 8-11 Sep 2003.
Pitfalls of Goodness-of-Fit from Likelihood, Joel Heinrich, ECONF C030908 (2003) MOCT001, arXiv:physics/0310167. PHYSTAT2003, SLAC, Stanford, Ca, USA, 8-11 Sep 2003.
Frequentist Hypothesis Testing with Background Uncertainty, K. S. Cranmer, ECONF C030908 (2003) WEMT004, arXiv:physics/0310108. PHYSTAT2003, SLAC, Stanford, Ca, USA, 8-11 Sep 2003.
Coverage of Confidence Intervals calculated in the Presence of Systematic Uncertainties, Jan Conrad et al., 2002. Advanced Statistical Techniques in Particle Physics, Durham, UK, 18-22 March 2002.
Limits setting in difficult cases and the Strong Confidence approach, Giovanni Punzi, 2002. Advanced Statistical Techniques in Particle Physics, Durham, UK, 18-22 March 2002.
An Application of the Strong Confidence to the CHOOZ Experiment with Frequentist Inclusion of Systematics, D. Nicolo nd G. Signorelli, 2002. Advanced Statistical Techniques in Particle Physics, Durham, UK, 18-22 March 2002.

18 - Frequentist Statistics - Applications

The Profiled Feldman-Cousins technique for confidence interval construction in the presence of nuisance parameters, M. A. Acero et al. (NOvA), arXiv:2207.14353, 2022.
Learning Uncertainties the Frequentist Way: Calibration and Correlation in High Energy Physics, Rikab Gambhir, Benjamin Nachman, Jesse Thaler, arXiv:2205.03413, 2022.
Nested sampling for frequentist computation: fast estimation of small $p$-values, Andrew Fowlie, Sebastian Hoof, Will Handley, Phys.Rev.Lett. 128 (2022) 021801, arXiv:2105.13923.
Sensitivity optimization of multichannel searches for new signals, Giovanni Punzi, arXiv:2011.11770, 2020.
Efficient Neutrino Oscillation Parameter Inference with Gaussian Process, Lingge Li, Nitish Nayak, Jianming Bian, Pierre Baldi, Phys.Rev. D101 (2020) 012001, arXiv:1811.07050.
Effect of Correlations Between Model Parameters and Nuisance Parameters When Model Parameters are Fit to Data, Byron Roe, arXiv:1309.6146, 2013.
Combining Neutrino Oscillation Experiments with the Feldman-Cousins Method, A. V. Waldron, M. D. Haigh, A. Weber, New J. Phys. 14 (2012) 063037, arXiv:1204.3450.
Limits, discovery and cut optimization for a Poisson process with uncertainty in background and signal efficiency: TRolke 2.0, J. Lundberg, J. Conrad, W. Rolke, A. Lopez, Comput. Phys. Commun. 181 (2010) 683-686, arXiv:0907.3450.
What is the probability that $\theta_{13}$ and CP violation will be discovered in future neutrino oscillation experiments?, Thomas Schwetz, Phys. Lett. B648 (2007) 54-59, arXiv:hep-ph/0612223.
Frequentistic quantum state discrimination, Giacomo Mauro D'Ariano, Massimiliano Federico Sacchi, Jonas Kahn, arXiv:quant-ph/0504048, 2005.
A Maximum Likelihood Analysis of the Low CMB Multipoles from WMAP, G. Efstathiou, Mon. Not. Roy. Astron. Soc. 348 (2004) 885, arXiv:astro-ph/0310207.
Variations on KamLAND: likelihood analysis and frequentist confidence regions, Thomas Schwetz, Phys. Lett. B577 (2003) 120, arXiv:hep-ph/0308003.
Statistics of the Chi-Square Type, with Application to the Analysis of Multiple Time-Series Power Spectra, P. A. Sturrock, M. S. Wheatland, Astrophys.J. (2003), arXiv:astro-ph/0307353.
A Modified chi^2-Test for CMB Analyses, J.A.Rubino-Martin, J.Betancort-Rijo, Mon. Not. Roy. Astron. Soc. 345 (2003) 221, arXiv:astro-ph/0307010.
Estimation of Goodness-of-Fit in Multidimensional Analysis Using Distance to Nearest Neighbor, Ilya Narsky, arXiv:physics/0306171, 2003.
Goodness-of-fit Statistics and CMB Data Sets, M. Douspis, J.G. Bartlett, A. Blanchard, Astron. Astrophys. 410 (2003) 11, arXiv:astro-ph/0305428.
Uncertainties of predictions from parton distributions. I: experimental errors, A. D. Martin, R. G. Roberts, W. J. Stirling, R. S. Thorne, Eur. Phys. J. C28 (2003) 455, arXiv:hep-ph/0211080.

19 - Frequentist Statistics - Applications - Talks

Multivariate Analysis from a Statistical Point of View, K. S. Cranmer, ECONF C030908 (2003) WEJT002, arXiv:physics/0310110. PHYSTAT2003, SLAC, Stanford, Ca, USA, 8-11 Sep 2003.

20 - CLs

CL$_s$ Method at Gaussian Limit to Present Searches, X. Qian, A. Tan, J. J. Ling, Y. Nakajima, C. Zhang, Nucl.Instrum.Meth. A827 (2016) 63, arXiv:1407.5052.
Confidence level computation for combining searches with small statistics, Thomas Junk, Nucl. Instrum. Meth. A434 (1999) 435-443, arXiv:hep-ex/9902006.

21 - CLs - Talks

Presentation of search results: The $\text{CL}_s$ technique, Alexander L. Read, J. Phys. G28 (2002) 2693-2704. Advanced Statistical Techniques in Particle Physics, Durham, UK, March 18-22, 2002.

22 - Bootstrap - Applications

Nonparametric Methods for Doubly Truncated Data, Bradley Efron, Vahe Petrosian, arXiv:astro-ph/9808334, 1998.
Application of the bootstrap statistical method to the tau decay mode problem, K. G. Hayes, Martin L. Perl, Bradley Efron, Phys. Rev. D39 (1989) 274.

23 - Talks

Likelihood ratio intervals with Bayesian treatment of uncertainties: coverage, power and combined experiments, Jan Conrad, Fredrik Tegenfeldt, arXiv:physics/0511055, 2005. PhyStat2005, Oxford, UK, Sept 2005, Imperial College Press.
Deriving laws from ordering relations, Kevin H. Knuth, J. Phys. G30 (2004) 957, arXiv:physics/0403031. Bayesian Inference and Maximum Entropy Methods in Science and Engineering, Jackson Hole WY 2003.
A Measure of the Goodness of Fit in Unbinned Likelihood Fits; End of Bayesianism?, Rajendran Raja, ECONF C030908 (2003) MOCT003, arXiv:physics/0401133. PHYSTAT2003, SLAC, Stanford, Ca, USA, 8-11 Sep 2003.
Asymmetric Errors, Roger Barlow, ECONF C030908 (2003) WEMT002, arXiv:physics/0401042. PHYSTAT2003, SLAC, Stanford, Ca, USA, 8-11 Sep 2003.
A unified approach to understanding statistics, Fred James, ECONF C030908 (2003) THAT002. PHYSTAT2003, SLAC, Stanford, Ca, USA, 8-11 Sep 2003.
Definition and treatment of systematic uncertainties in high energy physics and astrophysics, Pekka Sinervo, ECONF C030908 (2003) TUAT004. PHYSTAT2003, SLAC, Stanford, Ca, USA, 8-11 Sep 2003.
Likelihood inference in the presence of nuisance parameters, N. Reid, D.A.S. Fraser, ECONF C030908 (2003) THAT001. PHYSTAT2003, SLAC, Stanford, Ca, USA, 8-11 Sep 2003.
Signal Significance in Particle Physics, Pekka K. Sinervo, arXiv:hep-ex/0208005, 2002. Advanced Statistical Techniques in Particle Physics, Durham, March 2002. Advanced Statistical Techniques in Particle Physics'>Advanced Statistical Techniques in Particle Physics.
Signal significance in the presence of systematic and statistical uncertainties, S. I. Bityukov, JHEP 09 (2002) 060, arXiv:hep-ph/0207130. 8th International Workshop on Advanced Computing and Analysis Techniques in Physics Research (ACAT 2002), Moscow, Russia, 24-28 June 2002.
Systematic errors: Facts and fictions, Roger Barlow, arXiv:hep-ex/0207026, 2002. Advanced Statistical Techniques in Particle Physics, Durham, March 2002. Advanced Statistical Techniques in Particle Physics'>Advanced Statistical Techniques in Particle Physics.
Statistical practice at the Belle experiment, and some questions, Bruce Yabsley, J. Phys. G28 (2002) 2733, arXiv:hep-ex/0207006. Advanced Statistical Techniques in Particle Physics, Durham, March 2002. Advanced Statistical Techniques in Particle Physics'>Advanced Statistical Techniques in Particle Physics.
Uncertainties on parton related quantities, R. S. Thorne, J. Phys. G28 (2002) 2705-2716, arXiv:hep-ph/0205235. Advanced Statistical Techniques in Particle Physics, Durham, March 2002. Advanced Statistical Techniques in Particle Physics'>Advanced Statistical Techniques in Particle Physics.
Questions on uncertainties in parton distributions, R. S. Thorne et al., J. Phys. G28 (2002) 2717-2722, arXiv:hep-ph/0205233. Advanced Statistical Techniques in Particle Physics, Durham, March 2002. Advanced Statistical Techniques in Particle Physics'>Advanced Statistical Techniques in Particle Physics.
Uncertainties and Discovery Potential in Planned Experiments, S. I. Bityukov, N. V. Krasnikov, arXiv:hep-ph/0204326, 2002. Advanced Statistical Techniques in Particle Physics, Durham, March 2002. Advanced Statistical Techniques in Particle Physics'>Advanced Statistical Techniques in Particle Physics.
Answers to Fred James' 'Comments on a paper by Garzelli and Giunti', C. Giunti, 2002. Advanced Statistical Techniques in Particle Physics, Durham, March 2002. Advanced Statistical Techniques in Particle Physics'>Advanced Statistical Techniques in Particle Physics.
Comments on a paper by Garzelli and Giunti, F. James, 2002. Advanced Statistical Techniques in Particle Physics, Durham, March 2002. Advanced Statistical Techniques in Particle Physics'>Advanced Statistical Techniques in Particle Physics.
Credibility of Confidence Intervals, Dean Karlen, 2002. Advanced Statistical Techniques in Particle Physics, Durham, March 2002. Advanced Statistical Techniques in Particle Physics'>Advanced Statistical Techniques in Particle Physics.
Confidence Limits and their Errors, Rajendran Raja, 2002. Advanced Statistical Techniques in Particle Physics, Durham, March 2002. Advanced Statistical Techniques in Particle Physics'>Advanced Statistical Techniques in Particle Physics.

24 - History

The Fermi's Bayes Theorem, G. D'Agostini, Class.Quant.Grav. 23 (2006) 721-736, arXiv:physics/0509080.

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