In this chapter, we looked at stochastic processes and their applications. We looked at starting a random walk model. Random walks are mathematical models that are used to describe a path given by a succession of random steps, which, depending on the system we want to describe, may have a certain number of degrees of freedom or direction. We have learned how to deal with one-dimensional random walks, and we have seen how to write a code for the simulation of a random walk in the Python language.
Then we were introduced to Markov chains. To understand this topic, you were briefly introduced to probability calculation. The a priori probability, joint probability, and conditional probability were all defined, with examples of their calculation. We then moved on to the definition of Markov chains. A Markov chain is a mathematical model of a random phenomenon that evolves over...
