About
Bayesian statistics associate the idea of probability-the measure of the degree of belief that an event will occur - to the lack of knowledge, as it is commonly perceived intuitively. The Bayes' theorem becomes then the basic tool to evaluate the probability, combining (a priori) judgement and experimental information. This approach allows to treat in a logically consistent way all kinds of uncertainty. The lessons will deal with uncertainty arising from measurements: inference on the value of a physics quantity from experimental observations (examining in depth the cases of observations following Gaussian, binomialand poisson distributions); combinations of results; upper/lower limits and their combination; hypothesis tests versus probabilities of the hypotheses; systematic errors and the correlations they induce; simplified methods for routine applications (by-passing the explicit use of Bayes' theorem); type A and type B uncertainties (according to BIPM/ISO recovery of many standard methods, but deeping the transparency of the Bayesian reasoning; multidimensional unfolding..
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The talk
Credits and licence
- Institution
- CERN
- Department
- CERN Academic Training Lecture,358
- Speaker
- Giulio D'Agostini
- Year
- 1998
- Level
- Graduate
- Language
- English
- Source
- CERN
- Licence
- CERN, as CERN states it. CERN's terms
- Attribution
- © CERN. CERN provides its audiovisual media free of charge for educational and informational use, credited to CERN; this lecture is shown in CERN's own player.
- Who can watch
- CERN gives no rating. Invisible Network shows it to Teens profiles and older, judged from what the source says about it. How courses are placed