Maximum Likelihood Identification of an Information Matrix Under Constraints in a Corresponding Graphical Model
We address the problem of identifying the neighborhood structure of an undirected graph, whose nodes are labeled with the elements of a multivariate normal (MVN) random vector. A semi-definite program is given for estimating the information matrix under arbitrary constraints on its elements. More i...
| Main Author: | |
|---|---|
| Other Authors: | |
| Format: | Others |
| Published: |
Digital WPI
2017
|
| Subjects: | |
| Online Access: | https://digitalcommons.wpi.edu/etd-theses/128 https://digitalcommons.wpi.edu/cgi/viewcontent.cgi?article=1127&context=etd-theses |
| Summary: | We address the problem of identifying the neighborhood structure of an undirected graph, whose nodes are labeled with the elements of a multivariate normal (MVN) random vector. A semi-definite program is given for estimating the information matrix under arbitrary constraints on its elements. More importantly, a closed-form expression is given for the maximum likelihood (ML) estimator of the information matrix, under the constraint that the information matrix has pre-specified elements in a given pattern (e.g., in a principal submatrix). The results apply to the identification of dependency labels in a graphical model with neighborhood constraints. This neighborhood structure excludes nodes which are conditionally independent of a given node and the graph is determined by the non- zero elements in the information matrix for the random vector. A cross-validation principle is given for determining whether the constrained information matrix returned from this procedure is an acceptable model for the information matrix, and as a consequence for the neighborhood structure of the Markov Random Field (MRF) that is identified with the MVN random vector. |
|---|