HKUST Library Institutional Repository Banner

HKUST Institutional Repository >
Computer Science and Engineering >
CSE Technical Reports >

Please use this identifier to cite or link to this item:
Title: Effective dimensions of hierarchical latent class models
Authors: Zhang, Nevin Lianwen
Kočka, Tomáš
Keywords: Hierarchical latent class models
Tree-structured Bayesian networks
BIC score
Effective dimensions
HLC models
Issue Date: 2003
Series/Report no.: Computer Science Technical Report ; HKUST-CS03-03
Abstract: Hierarchical latent class (HLC) models are tree-structured Bayesian networks where leaf nodes are observed while internal nodes are latent. There are no theoretically well justified model selection criteria for HLC models in particular and Bayesian networks with latent nodes in general. Nonetheless, empirical studies suggest that the BIC score is a reasonable criterion to use in practice for learning HLC models. Empirical studies also suggest that sometimes model selection can be improved if standard model dimension is replaced with effective model dimension in the penalty term of the BIC score. Effective dimensions are difficult to compute. In this paper, we prove a theorem that relates the effective dimension of an HLC model to the effective dimensions of a number of latent class models. The theorem makes it computationally feasible to compute the effective dimensions of large HLC models. The theorem can also be used to compute the effective dimensions of general tree models.
Appears in Collections:CSE Technical Reports

Files in This Item:

File Description SizeFormat
tr0303.pdf109KbAdobe PDFView/Open

All items in this Repository are protected by copyright, with all rights reserved.