Summary
In this chapter, we learned how to define stochastic processes and understand the importance of using them to address numerous real-world problems. For instance, the operation of slot machines is based on the generation of random numbers, as are many complex data encryption procedures. Next, we introduced the concepts behind random number generation techniques. We explored the main methods of generating random numbers using practical examples in Python code. The generation of uniform and generic distributions was discussed. We also learned how to perform a uniformity test using the chi-squared method. Finally, we looked at the main functions available in Python for generating random numbers: random, seed, uniform, randint, choice, and sample.
In the next chapter, we will learn the basic concepts of probability theory. Additionally, we will learn how to calculate the probability of an event happening after it has already occurred, and then we will learn how to work with discrete...
