Markov chains are mathematical systems that represent transitions between different states, such as a baby’s activities like playing, eating, sleeping, and crying. These transitions are governed by probabilities, with a transition matrix used to track the likelihood of moving from one state to another. Markov chains are commonly used in computer simulations to model real-world phenomena like the frequency of rainy days. Google’s PageRank algorithm is an example of a Markov chain in action. This tool allows users to create their own Markov chains by adjusting the transition matrix. The power of Markov chains is evident in various fields like meteorology, ecology, and finance.
https://setosa.io/ev/markov-chains/