Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Knowledge representation and inference in similarity networks and Bayesian multinets
Artificial Intelligence
Learning Bayesian networks with local structure
Learning in graphical models
Graphical Models in Applied Multivariate Statistics
Graphical Models in Applied Multivariate Statistics
Representing and combining partially specified CPTs
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
A Bayesian approach to learning Bayesian networks with local structure
UAI'97 Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence
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Fienberg and Kim [1999. J. Amer. Statist. Assoc. 94 (445), 229-239] investigated the relationship between a log-linear model (LLM) and its conditional log-linear model (CLLM). While they considered a single conditional variable, we explored the relationship further considering multiple conditional variables. It is shown that we can find the exact model structure of an LLM from the sets of CLLMs of the LLM conditional on some sets of variables under a certain condition for the conditional sets. A graphical interpretation of the relation is presented when the LLM is graphical. The results are applied effectively for analyzing categorical real data from a lecture evaluation.