Markov Chain Models

Markov Chain Models are mathematical frameworks used to model systems that transition between states in a probabilistic manner. They are characterized by the Markov property, which states that the future state depends only on the current state and not on the sequence of events that preceded it. These models are widely used in various fields, including finance for stock price prediction, natural language processing for text generation, and operations research for decision-making processes. By representing states and transitions through a directed graph, Markov Chains can effectively capture the dynamics of complex systems.