Summary
Knowing the basics of probability theory in depth helps us to understand how random phenomena work. We discovered the differences between a priori, compound, and conditioned probabilities. We have also seen how Bayes' theorem allows us to calculate the conditional probability of a cause of an event, starting from the knowledge of the a priori probabilities and the conditional probability. Next, we analyzed some probability distributions, and how such distributions can be generated in Python.
In the next chapter, we will learn the basic concepts of Monte Carlo simulation and explore some of its applications. Then, we will discover how to generate a sequence of numbers that have been randomly distributed according to a Gaussian. Finally, we will take a look at the practical application of the Monte Carlo method in order to calculate a definite integral.
