Title :
The marginal likelihood for parameters in a discrete Gauss-Markov process
Author :
Bell, Bradley M.
Author_Institution :
Dept. of Bioeng., Washington Univ., Seattle, WA, USA
fDate :
3/1/2000 12:00:00 AM
Abstract :
We use Laplace´s method to approximate the marginal likelihood for parameters in a Gauss-Markov process. This approximation requires the determinant of a matrix whose dimensions are equal to the number of state variables times the number of time points. We reduce this to sequential evaluation of determinants and inverses of smaller matrices, we show this is a numerically stable method
Keywords :
Gaussian processes; Markov processes; adaptive Kalman filters; determinants; matrix inversion; Laplace method; approximation; determinant; dimensions; discrete Gauss-Markov process; inverses; marginal likelihood; matrix; numerically stable method; parameters; sequential evaluation; state variables; time points; Adaptive filters; Approximation algorithms; Filtering; Gaussian approximation; Gaussian processes; Noise measurement; Parameter estimation; State estimation; Symmetric matrices; Time measurement;
Journal_Title :
Signal Processing, IEEE Transactions on
