We present a new, efficient linear programming approach to probabilistic deduction from probabilistic knowledge bases over conjunctive events. We show that this approach enables us to solve the classical problem of probabilistic deduction along a chain of basic events in polynomial time in the length of the chain. We then elaborate how taxonomic knowledge can be exploited in our new approach for an increased efficiency. We also present important new results for the classical linear programming approach to probabilistic deduction under taxonomic knowledge.</p
Probabilistic models over strings have played a key role in developing methods allowing indels to be...
Probabilistic logical languages provide powerful formalisms for knowledge representation and learnin...
In probabilistic reasoning, the traditionally discrete domain has been elevated to the hybrid domain...
We present a new, efficient linear programming approach to probabilistic deduction from probabilisti...
We present a new, efficient linear programming approach to probabilistic deduction from probabilisti...
We elaborate locally complete inference rules for probabilistic deduction from taxonomic and probabi...
AbstractWe elaborate locally complete inference rules for probabilistic deduction from taxonomic and...
We elaborate locally complete inference rules for probabilistic deduction from taxonomic and probabi...
We present locally complete inference rules for probabilistic deduction from taxonomic and probabili...
We present locally complete inference rules for probabilistic deduction from taxonomic and probabili...
We show that probabilistic deduction with conditional constraints over basic events is NP-hard. We t...
We show that probabilistic deduction with conditional constraints over basic events is NP-hard. We t...
We study the problem of probabilistic deduction with conditional constraints over basic events. We s...
We present a new approach to probabilistic logic programs with a possible worlds semantics. Classica...
We introduce a new approach to probabilistic logic programming in which probabilities are defined ov...
Probabilistic models over strings have played a key role in developing methods allowing indels to be...
Probabilistic logical languages provide powerful formalisms for knowledge representation and learnin...
In probabilistic reasoning, the traditionally discrete domain has been elevated to the hybrid domain...
We present a new, efficient linear programming approach to probabilistic deduction from probabilisti...
We present a new, efficient linear programming approach to probabilistic deduction from probabilisti...
We elaborate locally complete inference rules for probabilistic deduction from taxonomic and probabi...
AbstractWe elaborate locally complete inference rules for probabilistic deduction from taxonomic and...
We elaborate locally complete inference rules for probabilistic deduction from taxonomic and probabi...
We present locally complete inference rules for probabilistic deduction from taxonomic and probabili...
We present locally complete inference rules for probabilistic deduction from taxonomic and probabili...
We show that probabilistic deduction with conditional constraints over basic events is NP-hard. We t...
We show that probabilistic deduction with conditional constraints over basic events is NP-hard. We t...
We study the problem of probabilistic deduction with conditional constraints over basic events. We s...
We present a new approach to probabilistic logic programs with a possible worlds semantics. Classica...
We introduce a new approach to probabilistic logic programming in which probabilities are defined ov...
Probabilistic models over strings have played a key role in developing methods allowing indels to be...
Probabilistic logical languages provide powerful formalisms for knowledge representation and learnin...
In probabilistic reasoning, the traditionally discrete domain has been elevated to the hybrid domain...
Add to ORKG