Characterized by random, unpredictable behavior, often described statistically rather than deterministically.Stochastic processes are systems whose behavior is inherently random, meaning that even with complete knowledge of the current state, the future is not fully determined. Instead of predicting exact outcomes, physicists describe these systems using probability distributions and statistical averages. Stochasticity appears everywhere in nature: from the jitter of a dust particle in water (Brownian motion) to the noise in electronic circuits and the fluctuations of stock markets.
The mathematical foundation of stochastic processes was laid by Louis Bachelier in his 1900 thesis on the theory of speculation, and independently by Albert Einstein in his 1905 paper on Brownian motion. The term 'stochastic' itself was coined by the mathematician Jerzy Neyman in the 1930s.
A stochastic system evolves through a sequence of random events. Mathematically, it is often modeled by adding a random 'noise' term to the equations of motion, leading to a Langevin equation or a Fokker-Planck equation for the probability distribution. The key insight is that while individual trajectories are unpredictable, the ensemble behavior — the average over many realizations — follows smooth, deterministic laws. This allows physicists to make robust predictions about the statistical properties of the system, such as its mean, variance, and correlation times.
Stochastic models are used in financial risk assessment, climate prediction, quantum measurement theory, and the design of error-correcting codes for communication systems.
The random noise in a resistor, called Johnson-Nyquist noise, is a stochastic process that sets a fundamental limit on the sensitivity of all electronic amplifiers.




