Kalman filtering-smoothing is a fundamental tool in statistical time series analysis. However, standard implementations of the Kalman filter-smoother require O(d3) time and O(d2) space per timestep, where d is the dimension of the state variable, and are therefore impractical in high-dimensional problems. In this paper we note that if a relatively small number of observations are available per ...
