Random Walk Models
Random walk models are mathematical concepts used to describe a path consisting of a series of random steps, often applied in fields like finance, physics, and computer science. They model processes where future values depend only on the current state plus a random component, making them fundamental for understanding stochastic processes and time series analysis. Common types include simple random walks, Brownian motion, and Lévy flights.
Developers should learn random walk models when working on simulations, financial modeling, or algorithms involving probabilistic behavior, such as in Monte Carlo methods or pathfinding. They are essential for predicting stock prices, modeling particle diffusion, or generating procedural content in games, providing a baseline for understanding more complex stochastic systems.