Bibliography

1
International Organization for Standardization (ISO), Guide to the expression of uncertainty in measurement, Geneva, Switzerland, 1993.

2
International Organization for Standardization (ISO), International vocabulary of basic and general terms in metrology, Geneva, Switzerland, 1993.

3
M. Tanabashi et al. (Particle Data Group), Phys. Rev. D98 , 030001 (2018) and 2019 update
http://pdg.lbl.gov/2019/listings/rpp2019-list-K-plus-minus.pdf.

4
G. Backenstoss et al., K$^-$ mass and K$^-$ polarizability from kaonic atoms, Phys. Lett. 43B 431 (1973).

5
S.C. Cheng et al., K$^-$ mass from kaonic atoms, Nucl. Phys. A254 381 (1975).

6
L.M. Barkov et al., The charged kaon mass measurement, Nucl. Phys. B148 53 (1979).

7
G.K. Lum et al., Kaonic mass by critical absorption of kaoni-atom x rays, Phys. Rev. D23 2522 (1981).

8
K.P. Gall et al., Precision measurements of the K$^-$ and $\Sigma^-$ masses, Phys. Rev. Lett. 60 186 (1988).

9
A.S. Denisov et al., New measurements of the mass of the K$^-$ meson JETPL 54 558 (1991), translated from ZETFP 54 557 (1991).

10
Yu.M. Ivanov, PhD Thesis, 1992 (see [11] for some hints on his contribution).

11
D.E. Groom et al. (Particle Data Group), The Eur. Phys. J. C15 (2000) 1
http://pdg.lbl.gov/2000/s010.pdf.

12
G. D'Agostini, Bayesian reasoning in data analysis. A critical introduction, World Scientific Publishing, 2003 (2013 paperback reprint recommended).

13
https://it.wikiquote.org/wiki/Giulio_Andreotti.

14
http://pdg.lbl.gov/2019/reviews/rpp2018-rev-history-plots.pdf.

15
G. D'Agostini, Sceptical combination of experimental results: General considerations and application to $\epsilon^\prime/\epsilon$, CERN-EP/99-139 and arXiv:hep-ex/9910036,
https://arxiv.org/abs/hep-ex/9910036.

16
V. Dose and W. von der Linden, Outliers tolerant parameter estimation, Proc. of the XVIII International Workshop on Maximum Entropy and Bayesian Methods, Garching (Germany), July 1998, eds. V. Dose, W. von der Linden, R. Fischer, and R. Preuss, (Kluwer Academic Publishers, Dordrecht, 1999), pp. 47-56.

17
C.F. Gauss, Theoria motus corporum coelestium in sectionibus conicis solem ambientum, Hamburg 1809, n.i 172-179; reprinted in Werke, Vol. 7 (Gota, Göttingen, 1871), pp 225-234.

18
C.F. Gauss Theory of the motion of the heavenly bodies moving about the sun in conic sections: a translation of Gauss's “Theoria motus.”, translated by C.H. Davies, Ulan Press, 2012 (first edition 1923).

19
http://www.roma1.infn.it/~dagos/history/Gauss_Gaussian.pdf.

20
G. D'Agostini, Fits, and especially linear fits, with errors on both axes, extra variance of the data points and other complications, arXiv:physics/0511182,
https://arxiv.org/abs/physics/0511182.

21
C. Andrieu et al., An introduction to MCMC for Machine Learning, Machine Learning 50 5-43 (2003), https://doi.org/10.1023/A:1020281327116.

22
A. Caldwell et al., BAT: The Bayesian Analysis Toolkit, Comput. Phys. Comm. 180 (2009) 2197-2209; J.Phys.Conf.Ser. 219 (2010) 032013; J.Phys.Conf.Ser. 331 (2011) 072040; https://bat.mpp.mpg.de/.

23
D. Lunn et al., The BUGS project: Evolution, critique and future directions, Statistics in Medicine 28 3049-3067 (2008), https://doi.org/10.1002/sim.3680.

24
M. Plummer, JAGS: A Program for Analysis of Bayesian Graphical Models Using Gibbs Sampling, Proceedings of the 3rd International Workshop on Distributed Statistical Computing (DSC 2003), March 20–22, Vienna, Austria. ISSN 1609-395X, http://mcmc-jags.sourceforge.net/.

25
The BUGS Project, http://www.mrc-bsu.cam.ac.uk/software/bugs/.

26
http://www.openbugs.net/w/Examples.

27
R Core Team (2018), R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria.
https://www.R-project.org/.

28
M. Plummer, rjags: Bayesian Graphical Models using MCMC.
R package version 4-10, https://CRAN.R-project.org/package=rjags.

29
T. Miasko, Python interface to JAGS library for Bayesian data analysis,
https://pypi.org/project/pyjags/.

30
M. Bognar, Probability distributions,
https://play.google.com/store/apps/details?id=com.mbognar.probdist,
https://apps.apple.com/us/app/probability-distributions/id889106396.

31
G. D'Agostini, Probability, propensity and probabilities of propensities (and of probabilities), AIP Conference Proceedings 1853, 030001 (2017);
https://doi.org/10.1063/1.4985350,
https://arxiv.org/abs/1612.05292.

32
G. D'Agostini, The Waves and the Sigmas (To Say Nothing of the 750 GeV Mirage), arXiv:1609.01668, https://arxiv.org/abs/1609.01668.

33
Min-Yi Chen, Radiative corrections of order $\alpha^2$ in muonic atoms of Heavy Nuclei, Phys. Rev. Lett. 34 (1975) 341.

34
L. Wilets and G.A. Rinker, Jr., Estimate of the $(Z\alpha)^2\alpha^2$ vacuum polarization term in muonic Pb, Phys. Rev. Lett. 34 (1975) 339.

35
G. D'Agostini, On a curious bias arising when the $\sqrt {\chi ^2/\nu }$ scaling prescription is first applied to a subsample of the individual results, paper in preparation.

36
G. D'Agostini, On the use of the covariance matrix to fit correlated data, Nucl. Instr. and Meth. in Phys. Res. A346 (1994) 306