Overview of basic concepts of probability theory (Literature: Barlow, chapters 3, 4 and 7.1). 1: Definitions of probability: freqentist and Bayesian, 25: Probability distributions, 6: Errors
Model estimation (Literature: Barlow, chapters 56) 7: Properties of estimators, 89: Method of least squares, 1012: Method of maximal probability, 13: Method of moments, 14: Estimation with constraints (kinematic approximation), 1516: Convolution and unfolding, 17: Estimation via Monte Carlo weight variation
Confidence (Literature: Barlow, chapter 7.2) 18: Confidence levels and intervals, 19: Neyman construction, Confidence intervals for binomial and Poisson data, 20: Confidence intervals for method of maximal probability and least squares method; confidence interval close to physical limit, 21: Multidimensional confidence interval
Decision making (Literature: Barlow, chapter 8) 22: Hypotheses, errors type I and II, statistical significance, 23: Neyman Pearson test, 24: Interpretation of results, nulhypothesis, statistical significance of signal
Selected multivariate analysis methods (Literature: Hastie, chapters 711) 25: Types of multivariate problems, choice of variables,
2627: Neural networks, 2829: Decision trees, 30: Overview of other methods
