  These lectures concern two topics that are becoming increasingly important in
the analysis of High Energy Physics (HEP) data: Bayesian statistics and
multivariate methods. In the Bayesian approach we extend the interpretation of
probability to cover not only the frequency of repeatable outcomes but also to
include a degree of belief. In this way we are able to associate probability
with a hypothesis and thus to answer directly questions that cannot be
addressed easily with traditional frequentist methods. In multivariate analysis
we try to exploit as much information as possible from the characteristics that
we measure for each event to distinguish between event types. In particular we
will look at a method that has gained popularity in HEP in recent years: the
boosted decision tree (BDT).
