We use the Metropolis-Hasting algorithm to sample a 2-dimensional empirical distribution.

We let your robot climb the Matterhorn with a Markov Chain Monte Carlo walk

Using the coin flipping example, we give some arguments WHY the use of distribution vectors can be helpful as a preparation for Monte Carlo Markov Chain models and others and how this changes the role of medical researchers.

We provide a simple sampling engine which allows to generate random numbers that are distributed as an empirical and arbitrary distribution given as a data array.

By summarizing the rules of probability we build a bridge to the Bayesian formula