Random Walk
A random walk is a mathematical concept describing a path consisting of a sequence of random steps, where each step is independent and identically distributed. It is widely used in fields like physics, finance, and computer science to model stochastic processes, such as particle motion, stock price movements, or algorithm behavior. The concept helps analyze systems with inherent randomness and predict long-term trends or distributions.
Developers should learn random walks when working on simulations, machine learning algorithms, or financial modeling, as they provide a foundation for understanding probabilistic systems. For example, in reinforcement learning, random walks can model exploration strategies, while in network analysis, they help study graph traversal and node ranking. It's also essential for implementing Monte Carlo methods or analyzing time-series data in data science applications.