Markov Chains Clearly Explained! Part - 1

The post introduces Markov chains, a concept used in various fields such as statistics, biology, economics, physics, and machine learning. It explains how Markov chains rely on the current state to predict future states, using a restaurant example to illustrate transitions between states. The importance of the Markov property and stationary distribution is highlighted, along with a method to find these distributions using linear algebra. The post concludes by validating the theoretical results with a simulation and invites readers to engage for more content on advanced Markov chain topics.