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 also saw 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 final part of the chapter, we introduced the basics of synthetic data generation by analyzing a practical case of data augmentation with the Keras library. Finally, we explored power analysis for statistical tests.

In the next chapter, we will learn about 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 Gaussian. Finally, we will take...