Advances in the Dempster-Shafer theory of evidence
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
Intelligent Data Analysis: An Introduction
Intelligent Data Analysis: An Introduction
Bayes' Theorem Revised - The Rough Set View
Proceedings of the Joint JSAI 2001 Workshop on New Frontiers in Artificial Intelligence
Bayes' Theorem Revised - The Rough Set View
Proceedings of the Joint JSAI 2001 Workshop on New Frontiers in Artificial Intelligence
A Rough Set Based Approach for ECG Classification
Transactions on Rough Sets IX
An approach to a rough set based disease inference engine for ECG classification
RSCTC'06 Proceedings of the 5th international conference on Rough Sets and Current Trends in Computing
Transactions on Rough Sets III
Bayesian rough set model: A further investigation
International Journal of Approximate Reasoning
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Rough set theory offers new insight into Bayes' theorem. The look on Bayes' theorem offered by rough set theory is completely different from that used in the Bayesian data analysis philosophy. It does not refer either to prior or posterior probabilities, inherently associated with Bayesian reasoning, but it reveals some probabilistic structure of the data being analyzed. It states that any data set (decision table) satisfies total probability theorem and Bayes' theorem. This property can be used directly to draw conclusions from data without referring to prior knowledge and its revision if new evidence is available. Thus in the presented approach the only source of knowledge is the data and there is no need to assume that there is any prior knowledge besides the data. We simply look what the data are telling us. Consequently we do not refer to any prior knowledge which is updated after receiving some data.