Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic Expert Systems
an entropy-driven system for construction of probabilistic expert systems from databases
UAI '90 Proceedings of the Sixth Annual Conference on Uncertainty in Artificial Intelligence
Learning the structure of dynamic probabilistic networks
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Learning belief networks in domains with recursively embedded pseudo independent submodels
UAI'97 Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence
Critical remarks on single link search in learning belief networks
UAI'96 Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence
Local Score Computation in Learning Belief Networks
AI '01 Proceedings of the 14th Biennial Conference of the Canadian Society on Computational Studies of Intelligence: Advances in Artificial Intelligence
Complexity measurement of fundamental pseudo-independent models
International Journal of Approximate Reasoning
RSFDGrC'05 Proceedings of the 10th international conference on Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing - Volume Part I
Foundation for the new algorithm learning pseudo-independent models
ECSQARU'05 Proceedings of the 8th European conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
Hi-index | 0.00 |
Most algorithms to learn belief networks use single-link lookahead search to be efficient. It has been shown that such search procedures are problematic when applied to learning pseudo-independent (PI) models. Furthermore, some researchers have questioned whether PI models exist in practice. We present two non-trivial PI models which derive from a social study dataset. For one of them, the learned PI model reached ultimate prediction accuracy achievable given the data only, while using slightly more inference time than the learned non-PI model. These models provide evidence that PI models are not simply mathematical constructs. To develop efficient algorithms to learn PI models effectively we benefit from studying and understanding such models in depth. We further analyze how multiple PI submodels may interact in a larger domain model. Using this result, we show that the RML algorithm for learning PI models can learn more complex PI models than previously known.