The course objective is to introduce Bayesian probability theory as extended logic. After a quick review of Boolean algebra,
we derven Bayes theorem from Cox theorem. We will then introduce fundaments of parameter estimation as well as Bayesian
model selection. Further, we will introduce the maximum entropy principle and we will discuss some of the most common probability
distributions that can be derived from it.
We will introduce some fundamental concepts of stochastic processes and discuss them within the context of the maximum
entropy principle. The course will also introduce practical examples of algorithms, such as markov chain monte carlo and nested sampling,
that are relevant for the solution of inference problems.