Information-based objective functions for active data selection
Neural Computation
An Investigation of the Laws of Thought
An Investigation of the Laws of Thought
A Mathematical Theory of Communication
A Mathematical Theory of Communication
Observer-participant models of neural processing
IEEE Transactions on Neural Networks
Fuzzy lattice reasoning (FLR) classifier and its application for ambient ozone estimation
International Journal of Approximate Reasoning
Fuzzy lattice and function approximation in image processing
International Journal of Intelligent Systems Technologies and Applications
The dichotomy of probabilistic inference for unions of conjunctive queries
Journal of the ACM (JACM)
Is uncertain logical-matching equivalent to conditional probability?
Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval
Revisiting Exhaustivity and Specificity Using Propositional Logic and Lattice Theory
Proceedings of the 2013 Conference on the Theory of Information Retrieval
The inclusion-exclusion rule and its application to the junction tree algorithm
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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Bayesian probability theory is an inference calculus, which originates from a generalization of inclusion on the Boolean lattice of logical assertions to a degree of inclusion represented by a real number. Dual to this lattice is the distributive lattice of questions constructed from the ordered set of down-sets of assertions, which forms the foundation of the calculus of inquiry-a generalization of information theory. In this paper we introduce this novel perspective on these spaces in which machine learning is performed and discuss the relationship between these results and several proposed generalizations of information theory in the literature.