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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.

Describes events occurring independently over time at a constant average rate, often used in queuing theory.

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

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

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

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

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

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