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

被引：22

作者：

Bell, BM ^{[1
]}

机构：

[1] Univ Washington, Appl Phys Lab, Seattle, WA 98195 USA

[2] Univ Washington, Dept Bioengn, Seattle, WA 98195 USA

来源：

IEEE TRANSACTIONS ON SIGNAL PROCESSING | 2000年 / 48卷 / 03期

关键词：

adaptive Kalman-Bucy filtering; Gauss-Markov process; symmetric block tridiagonal;

D O I：

10.1109/78.824682

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 [电气工程]; 0809 [电子科学与技术];

摘要：

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.

引用

页码：870 / 873

页数：4

共 14 条

[1]

ANDERSON BDO, 1987, OPTIMAL FILTERING

[2]

[Anonymous], 1993, MATH OPERATIONS RES

[3]

Beal S. L., 1998, NONMEM USERS GUIDE 7

[4]

THE ITERATED KALMAN SMOOTHER AS A GAUSS-NEWTON METHOD [J].