Markov process

A Markov process is a way of modeling a system that moves between different states over time, where the next state depends only on the current one, not the past history. In other words, the process has no memory beyond the present. This property makes it useful for predicting future behavior based solely on the current situation. Examples include weather patterns, stock prices, and board game movements. Markov processes help us understand and simulate systems where each step depends only on where we are now, making analysis and forecasts more manageable.