Gauss-Markov processes

A Gauss-Markov process is a type of mathematical model used to describe systems where current values depend on previous ones, with uncertainty following a normal (bell-curve) distribution. "Gauss" indicates the process's randomness is Gaussian, while "Markov" means the future state depends only on the present, not the past. Such models are widely used in areas like finance, engineering, and science to predict and analyze phenomena that evolve over time with random fluctuations, making them essential tools for understanding dynamic systems with memoryless stochastic behavior.