Markov Chain Algorithms in Every Aspect of Life

Markov Chains are far more than an abstract mathematical theory. They are foundational to understanding how probability influences a variety of systems. Whether it’s predicting the price of gold, modeling weather, or improving algorithms like Google’s PageRank, Markov Chains offer a way to represent complex systems using simple transitions between states.

The origin of the Markov process dates back to the late 19th century and is attributed to the Russian mathematician Andrey Andreyevich Markov (1856–1922). Markov is regarded as the founder of these processes, which bear his name. The emergence of the Markov process is closely related to his contributions to probability theory.

Markov studied at St. Petersburg University, where he made significant contributions to mathematics. He focused on probability theory, particularly the relationships between dependent events.

In 1906, Markov published a paper examining the effects of dependent events on probability theory. In this paper, he described a process that models how events can be dependent on each other, with each event relying…