Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic Expert Systems
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
In Pursuit of Patterns in Data Reasoning from Data The Rough Set Way
TSCTC '02 Proceedings of the Third International Conference on Rough Sets and Current Trends in Computing
Generalized Decision Algorithms, Rough Inference Rules, and Flow Graphs
TSCTC '02 Proceedings of the Third International Conference on Rough Sets and Current Trends in Computing
Flow graphs and decision algorithms
RSFDGrC'03 Proceedings of the 9th international conference on Rough sets, fuzzy sets, data mining, and granular computing
The lumière project: Bayesian user modeling for inferring the goals and needs of software users
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Display of information for time-critical decision making
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
On the implication problem for probabilistic conditional independency
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Inference and Reformation in Flow Graphs Using Granular Computing
RSEISP '07 Proceedings of the international conference on Rough Sets and Intelligent Systems Paradigms
Rough Set Flow Graphs and Max - * Fuzzy Relation Equations in State Prediction Problems
RSCTC '08 Proceedings of the 6th International Conference on Rough Sets and Current Trends in Computing
Extended Pawlak's Flow Graphs and Information Theory
Transactions on Computational Science V
Knowledge discovery by rough sets mathematical flow graphs and its extension
AIA '08 Proceedings of the 26th IASTED International Conference on Artificial Intelligence and Applications
RSKT'07 Proceedings of the 2nd international conference on Rough sets and knowledge technology
Interpretation of extended Pawlak flow graphs using granular computing
Transactions on rough sets VIII
An extension of rough set approximation to flow graph based data analysis
RSCTC'10 Proceedings of the 7th international conference on Rough sets and current trends in computing
An extension of pawlak's flow graphs
RSKT'06 Proceedings of the First international conference on Rough Sets and Knowledge Technology
An interpretation of flow graphs by granular computing
RSCTC'06 Proceedings of the 5th international conference on Rough Sets and Current Trends in Computing
An efficient algorithm for inference in rough set flow graphs
Transactions on Rough Sets V
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Pawlak recently introduced rough set flow graphs (RSFGs) as a graphical framework for reasoning from data. Each rule is associated with three coefficients, which have been shown to satisfy Bayes' theorem. Thereby, RSFGs provide a new perspective on Bayesian inference methodology. In this paper, we show that inference in RSFGs takes polynomial time with respect to the largest domain of the variables in the decision tables. Thereby, RSFGs provide an efficient tool for uncertainty management. On the other hand, our analysis also indicates that a RSFG is a special case of conventional Bayesian network and that RSFGs make implicit assumptions regarding the problem domain.