In this chapter, sequential sampling techniques are considered. Kalman filtering is viewed in terms of probabilistic arguments as a special case of a linear dynamic system, where the involved variables follow Gaussian distributions. Particle filtering techniques are then considered as a vehicle to treat more general nonlinear models and/or non-Gaussian random variables. They are introduced as a special instance of the more general family of sequential sampling methods. Different schemes are discussed such as the generic particle and the auxiliary particle filtering algorithms.