A recursive algorithm for estimating linear models with both constant and time-varying parameters is derived by maximization of a likelihood function. Recursive formulas are also derived for derivatives of the likelihood function; the derivatives are needed for numerical evaluation of some...

The problem of simultaneously identifying and controlling a time-varying, perfectly-observed linear system is posed. The parameters are assumed to obey a Markov structure and are estimated with a Kalman filter. The problem can be solved conceptually by dynamic programming, but even with a...