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Review Articles

Bayes and Frequentism: a particle physicist’s perspective

Pages 1-16
Received 29 Oct 2012
Accepted 04 Dec 2012
Published online: 18 Jan 2013
 
Translator disclaimer

In almost every scientific field, an experiment involves collecting data and then analysing it. The analysis stage will often consist in trying to extract some physical parameter and estimating its uncertainty; this is known as Parameter Determination. An example would be the determination of the mass of the top quark, from data collected from high energy proton–proton collisions. A different aim is to choose between two possible hypotheses. For example, are data on the recession speed s of distant galaxies proportional to their distance d, or do they fit better to a model where the expansion of the Universe is accelerating? There are two fundamental approaches to such statistical analyses – Bayesian and Frequentist. This article discusses the way they differ in their approach to probability, and then goes on to consider how this affects the way they deal with Parameter Determination and Hypothesis Testing. The examples are taken from everyday life and from Particle Physics.

Additional information

Acknowledgements

I would like to thank Bob Cousins, David van Dyk, Luc Demortier and Roberto Trotta for their advice on various sections of this article. I am grateful to the following for permission to reproduce diagrams: Profs. Stephen Stigler and Maurizio Cornalba (fig. 1), Prof. Joel Heinrich (fig. 4) and to CERN (fig. 5).

Notes

1. The ‘!’ symbol in Equations (1) and (2) not only expresses surprise (‘Wow! These equations look very similar), but it also denotes the factorial.

2. This definition does not provide a unique range. The one we show has a probability of 16% on either side of the selected region, which is then known as a central interval. An alternative would be to have the whole of the 32% on the left hand side of the confidence interval; this would be useful for producing upper limits on .

3. If is a discrete variable, such as a number of events, then ‘above' is replaced by ‘greater than or equal to', and correspondingly for ‘below’.

4. This stands for ‘confidence level of signal', but it is a poor notation, as CLs is in fact a ratio of p-values, which is itself not even a p-value, let alone a confidence level.

5. For the purpose of model comparison, any parameters are considered as nuisance parameters, even if they are physically meaningful, e.g. the parameters of a straight line fit, the mass of the Higgs boson, etc.

6. This is an example of Occam’s Razor, whereby a simpler hypothesis may be favoured over a more complex one.

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