Markov Process
A Markov process is a stochastic process that satisfies the Markov property, meaning the future state depends only on the current state and not on the sequence of events that preceded it. It is widely used in probability theory, statistics, and various applied fields to model systems with random transitions between states over time. Common types include discrete-time Markov chains (DTMC) and continuous-time Markov chains (CTMC), which are foundational for analyzing sequential data and dynamic systems.
Developers should learn Markov processes when working on projects involving probabilistic modeling, such as natural language processing (e.g., text generation, speech recognition), reinforcement learning, queueing theory, or financial modeling. They are essential for building systems that predict future states based on current data, like recommendation algorithms, game AI, or simulations of random processes in network traffic or biological systems.