By Olaf Behnke, Kevin Kroninger, Gregory Schott, Thomas Schorner-Sadenius
This useful consultant covers the fundamental projects in statistical info research encountered in excessive power physics and offers entire recommendation for regular questions and difficulties. the fundamental equipment for inferring effects from facts are offered in addition to instruments for complex initiatives comparable to enhancing the signal-to-background ratio, correcting detector results, opting for systematics and so on. Concrete purposes are mentioned in research walkthroughs. every one bankruptcy is supplemented via various examples and routines and via an inventory of literature and proper hyperlinks. The booklet pursuits a vast readership in any respect occupation degrees - from scholars to senior researchers. An accompanying web site presents extra algorithms in addition to updated details and links.
* loose ideas guide on hand for academics at www.wiley-vch.de/supplements/
Chapter 1 primary suggestions (pages 1–26): Roger Barlow
Chapter 2 Parameter Estimation (pages 27–73): Dr. Olaf Behnke and Lorenzo Moneta
Chapter three speculation checking out (pages 75–105): Dr. Gregory Schott
Chapter four period Estimation (pages 107–151): Luc Demortier
Chapter five category (pages 153–186): Helge Voss
Chapter 6 Unfolding (pages 187–225): Volker Blobel
Chapter 7 restricted suits (pages 227–262): Benno List
Chapter eight how you can take care of Systematic Uncertainties (pages 263–296): Rainer Wanke
Chapter nine concept Uncertainties (pages 297–328): Markus Diehl
Chapter 10 Statistical equipment customary in excessive strength Physics (pages 329–356): Carsten Hensel and Dr. Kevin Kroninger
Chapter eleven research Walk?Throughs (pages 357–379): Aart Heijboer and Ivo van Vulpen
Chapter 12 functions in Astronomy (pages 381–407): Harrison B. Prosper
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Extra resources for Data Analysis in High Energy Physics, A Practical Guide to Statistical Methods
This is a hard and veriﬁable number, essential for the correct adjustment of the premium paid. However, it is not an intrinsic probability of the person concerned: you cannot say that a particular client has this number attached to them as a property in the same way that their height and weight are. The client belongs not just to this ensemble (insured 40-yr-old males) but to many others: 40-yr-old males, non-smoking 40-yr-old males, non-smoking professional lion tamers – and for each of these ensembles there will be a different number.
N. (1950) Foundations of the Theory of Probability, Chelsea Publishing Company. 8 von Mises, R. (1957) Probability, Statistics and Truth, Dover Publications. 9 de Finetti, B. (1974) Theory of Probability, John Wiley & Sons. 10 Bayes, T. (1763) An essay towards solving a problem in the doctrine of chances. Philos. Trans. R. , 53, 370. 11 Jeffreys, H. (1966) Theory of Probability, Oxford University Press. , and Sun, D. (2009) The formal deﬁnition of reference priors. Ann. , 37, 905. 1 Parameter Estimation in High Energy Physics: Introductory Words The estimation of parameters from observed distributions, a process also called ﬁtting, is one of the fundamental data analysis tasks in experimental high energy physics; it is applied in almost every step of a measurement.
It satisﬁes, of course, the Kolmogorov axioms. 17 18 1 Fundamental Concepts Where the classical deﬁnition is valid it leads to the same results. But there is an important philosophical difference. The probability P(A) is not some intrinsic property of A, it also depends on the way the sampling is done: on how the collective or ensemble of total possible outcomes has been constructed. 1%. This is a hard and veriﬁable number, essential for the correct adjustment of the premium paid. However, it is not an intrinsic probability of the person concerned: you cannot say that a particular client has this number attached to them as a property in the same way that their height and weight are.