The marginal likelihood for parameters in a discrete Gauss-Markovprocess

Authors:
B.M. Bell
Affiliations:
Dept. of Bioeng., Washington Univ., Seattle, WA
Venue:
IEEE Transactions on Signal Processing
Year:
2000

Citing 0
Cited 5

Optimal smoothing of non-linear dynamic systems via Monte Carlo Markov chains

Automatica (Journal of IFAC)
An inequality constrained nonlinear Kalman-Bucy smoother by interior point likelihood maximization

Automatica (Journal of IFAC)
Brief paper: Fast computation of smoothing splines subject to equality constraints

Automatica (Journal of IFAC)
Distributed Kalman smoothing in static Bayesian networks

Automatica (Journal of IFAC)
Sparse/robust estimation and Kalman smoothing with nonsmooth log-concave densities: modeling, computation, and theory

The Journal of Machine Learning Research

Quantified Score

Hi-index	35.69

Visualization

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