Hierarchical latent class models for cluster analysis
Eighteenth national conference on Artificial intelligence
Effective dimensions of hierarchical latent class models
Journal of Artificial Intelligence Research
Dimension correction for hierarchical latent class models
UAI'02 Proceedings of the Eighteenth conference on Uncertainty in artificial intelligence
On the geometry of Bayesian graphical models with hidden variables
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Asymptotic model selection for directed networks with hidden variables*
UAI'96 Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence
Automated analytic asymptotic evaluation of the marginal likelihood for latent models
UAI'03 Proceedings of the Nineteenth conference on Uncertainty in Artificial Intelligence
| Hi-index | 0.00 |
Model complexity is an important factor to consider when selecting among Bayesian network models. When all variables are observed, the complexity of a model can be measured by its standard dimension, i.e., the number of linearly independent network parameters. When latent variables are present, however, standard dimension is no longer appropriate and effective dimension should be used instead [Proc. 12th Conf. Uncertainty Artificial Intell. (1996) 283]. Effective dimensions of Bayesian networks are difficult to compute in general. Work has begun to develop efficient methods for calculating the effective dimensions of special networks. One such method has been developed for partially observed trees [J. Artificial Intell. Res. 21 (2004) 1]. In this paper, we develop a similar method for partially observed polytrees.