The estimation task is classified as filtering, smoothing, and prediction, depending on when the estimation and the observation incorporation are made. Basic techniques of filtering and smoothing are introduced. Characteristics and formulations of various filters and smoothers are discussed, including the Kalman filter, extended Kalman filter, fixed-point smoother, fixed-lag smoother, and fixed-interval smoother. Bayesian perspectives of filtering and smoothing are also discussed, especially on joint smoother and marginal smoother.