Theoretical foundations of detection and estimation theories. Bayesian decision theory with applications to signal detection in discrete time; concept of sufficient statistic and minimum variance unbiased estimation; Bayesian estimation; best linear unbiased estimation; Kalman filtering and its variants; filtering, smoothing and prediction applications across various engineering fields.