PHYSTAT-Gamma 2022

Session

Pre-School on Statistical methods

27 Sept 2022, 14:00

19. Statistics 101: A very fast introduction I

Glen Cowan

27/09/2022, 14:00

Probability and Bayes theorem, Frequentist and Bayesian statistics, likelihood
function, parameter estimation and properties of estimators, maximum likelihood
estimators (MLE), information inequality, asymptotic properties of MLE,
variance of MLE

20. Statistics 101: A very fast introduction II

Glen Cowan

27/09/2022, 14:50

Frequentist hypothesis tests, significance level and power of a test, Neyman-Pearson lemma/likelihood ratio, goodness of fit, p values and significances, confidence interval from a test, coverage, confidence intervals and selected problems (e.g. limits near the boundary of the parameter space), Wilk's theorem and confidence regions

21. Selected applications of 'Statistics 101' in HE/VHE gamma-ray astronomy

Ullrich Schwanke

27/09/2022, 15:55

Error propagation, combination of stat+syst errors, profile likelihood, inter-experiment combination of likelihoods, trial factors, binned likelihood and applications in gamma-ray astronomy (Poisson Maximum Likelihood Estimation, On-Off Likelihood statistics)

22. Introduction to Machine Learning in astroparticle physics

Tim Ruhe

27/09/2022, 16:30

This very short introduction will summarize basic machine learning concepts and introduce and discuss a few feature selection and learning algorithms. The selected algorithms include: Naive Bayes, Nearest Neighbour Methods, Decicison Trees, Ensemble Methods and Neural Networks. Furthermore, the talk will address the selection of appropriate input variables as well as possibilities to exclude...