Stochastic Process
A stochastic process is a mathematical model that describes a collection of random variables indexed by time or space, representing the evolution of a system subject to random influences. It provides a framework for analyzing phenomena where uncertainty and randomness play a key role, such as stock prices, weather patterns, or queue lengths. Stochastic processes are fundamental in probability theory and applied fields like finance, physics, and engineering.
Developers should learn stochastic processes when building systems involving randomness, uncertainty, or time-dependent probabilistic behavior, such as financial modeling, risk assessment, or simulation of complex systems. It is essential for applications in quantitative finance (e.g., option pricing), machine learning (e.g., Markov chains in reinforcement learning), and operations research (e.g., queueing theory). Understanding stochastic processes helps in designing algorithms that account for variability and make probabilistic predictions.