Efficient computation of gradient and hessian of likelihood function in linear dynamic systems

This technical note describes a computationally efficient procedure to determine the first and second gradients of the likelihood function for parameter estimation in linear dynamic systems. The results presented here are extensions, of the sensitivity functions reduction procedure of [1]. An operation count shows the value of the new algorithm.