About
Learning to love the errror matrix lecture : Learning to love the errror matrix Introductory remarks. Conditional probability. Statistical and systematic errors. Combining results Binomial, Poisson and 1-D Gaussian 2-D Gaussian and the error matrix. Understanding the covariance. Using the error matrix. Estimating the error matrix. Combining correlated measurements Parameter determination by likelihood Do's and don'ts lecture : Parameter determination by likelihood : Do's and don'ts Introduction to likelihood. Error estimate. Simple examples: (1) Breit Wigner (2) Lifetime Binned and unbinned likelihood Several parameters Extended maximum likelihood. Common misapprehensions: Normalisation delta(lnL) = 1/2 rule and coverage Integrating the likelihood Unbinned L_max as goodness of fit Punzi effect Chi-squared and hypothesis testing lecture : Chi-squared and hypothesis testing Basic idea. Error estimates. Several parameters Correlated errors on y. Errors on x and y. Goodness of fit. Degrees of freedom. Why assymptotic? Errors of first kind and second kind. THE paradox Kinematic fits. Toy example. Bayes, Frequentism and limits lecture : Bayes, Frequentism and limits Bayes and frequentist probability. Everyday examples. Prob(data;theory) is not equal to Prob(theory;data) Bayes theorem. Bayesian prior. When priors are and are not important. Frequentist confidence intervals, and their properties. Limits calculations by Bayes, Neyman construction and Feldman-Cousins Summary of Bayes and Frequentist approaches Discovery and p-values lecture : Discovery and p-values Distinguishing a peak, a goof, and a statistical fluctuation Why 5 sigma for discovery? Blind analyses What p-values are and what they are not Combining p-values Simultaneous optimisation for discovery and exclusion Incorporating systematic effects.
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The talk
Credits and licence
- Institution
- CERN
- Department
- CERN Academic Training Lecture
- Speaker
- Louis Lyons
- Year
- 2006
- Level
- Graduate
- Language
- English
- Subjects
- Mathematics, Physics
- 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 Tweens profiles and older, judged from what the source says about it. How courses are placed