Probability, Frequentist and Bayesian. Confidence. Bayes theorem. Priors
and posteriors
Probability distributions (Binomial and Poisson) and Probability
distribution functions (Gaussian). Expectation values. Hypothesis testing
Basics Estimation. Maximum likelihood. Least squares. Fitting histograms. Chi squared and goodness of fit. p-value
Intricately linked with the estimation of parameters is the question on how
to obtain meaningful uncertainties on the obtained parameters. In these two
lectures, we will look at this problem in detail and discuss confidence
intervals on extracted parameters. We will start with a simple case of a
counting experiment following Poissonian statistics. This will lead us to
the coverage of...
An introduction to the R language. The aim of this is to explain the
minimum about R that everybody needs to know, and hopefully encourage those
that would benefit from learning and using the language to do so.
For Lecture 6, it will be helpful (though not essential) to download R
beforehand from https://cran.r-project.org/ [cran.r-project.org]
or, if you prefer working in an IDE,...
We will discuss questions collected at:
https://docs.google.com/document/d/1id8UczLtlOWlhI7LMXK8cU37AE_wAbIQzyo1nodgxWI/edit
