The marginal likelihood for parameters in a discrete Gauss-Markov process
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.
