An Introduction to Markov Chains, their applications, and how to use Monte Carlo Simulations in Python
The Markov chain is a central concept in mathematics and stochastics and is used to predict the probability of certain states in stochastic processes. The central feature of such systems is the so-called “memorylessness” since the probability of each event depends only on the current state of the system and not on the past.
In this article, we take a closer look at the central properties of the Markov chain and go into the mathematical representation in detail. We also talk about real examples and simulate such a state model in Python.
A Markov chain is a central model in probability theory that deals with sequences of random events. The central feature of this chain is that each probability of an event depends exclusively on the state the system is currently in. The previous events, on the other hand, are completely irrelevant to the probability of the next step. More precisely, a Markov chain is a process that satisfies the Markov property, as it states that the future behavior of a system does not depend on the…