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Used for probabilistic modeling in machine learning for making predictions about complex, non-linear systems.

A sequential process where the next value is a random step from the current one, related to diffusion phenomena.

A special case of Brownian motion, representing continuous-time random walks with Gaussian increments.

A partial differential equation describing the time evolution of the probability density function of the velocity of a particle under the influence of drag forces and random forces.

Used to model systems that transition from one state to another, where the next state only depends on the current state.

Models mean-reverting behavior suitable for temperature variation or interest rates.

Models random continuous movement, often used to simulate particle paths or stock market fluctuations.

A random walk with steps of varying sizes, often heavy-tailed, used to model foraging patterns and stock market movements.

Combines Gaussian diffusion with Poisson jumps, suitable for option pricing and financial modeling with discontinuities.