Add to ORKGProbability as an Alternative to Boolean LogicWhile logic is the mathematical foundation of rational reasoning and the fundamental principle of computing, it is restricted to problems where information is both complete and certain. However, many real-world problems, from financial investments to email filtering, are incomplete or uncertain in nature. Probability theory and Bayesian computing together provide an alternative framework to deal with incomplete and uncertain data. Decision-Making Tools and Methods for Incomplete and Uncertain DataEmphasizing probability as an alternative to Boolea
Uncertainty is a fundamental and irreducible aspect of our knowledge about the world. Until recently...
Given the complexity of the domains for which we would like to use computers as reasoning engines, ...
Abstract. This paper presents a revised comparison of Bayesian logic programs (BLPs) and stochastic ...
Bayesian networks provide an elegant formalism for representing and reasoning about uncertainty usin...
Probability can be viewed as a multi-valued logic that extends binary Boolean propositional logic t...
Probabilistic Logic Programming extends Logic Programming by enabling the representation of uncertai...
I examine the idea of incorporating probability into logic for a logic of practical reasoning. I int...
Abstract. Two approaches to logic programming with probabilities emerged over time: bayesian reasoni...
By identifying and pursuing analogies between causal and logical in uence I show how the Bayesian ne...
First-order probabilistic models are recognized as efficient frameworks to represent several realwor...
In recent years, there has been a significant interest in integrating probability theory with first ...
Decision support systems have emerged over five decades ago to serve decision makers in uncertain co...
AbstractProbability is usually closely related to Boolean structures, i.e., Boolean algebras or prop...
We describe how to combine probabilistic logic and Bayesian networks to obtain a new frame-work ("Ba...
The Bayesian theorem was formulated in the 18th century and has been adopted as the theoretical basi...
Uncertainty is a fundamental and irreducible aspect of our knowledge about the world. Until recently...
Given the complexity of the domains for which we would like to use computers as reasoning engines, ...
Abstract. This paper presents a revised comparison of Bayesian logic programs (BLPs) and stochastic ...
Bayesian networks provide an elegant formalism for representing and reasoning about uncertainty usin...
Probability can be viewed as a multi-valued logic that extends binary Boolean propositional logic t...
Probabilistic Logic Programming extends Logic Programming by enabling the representation of uncertai...
I examine the idea of incorporating probability into logic for a logic of practical reasoning. I int...
Abstract. Two approaches to logic programming with probabilities emerged over time: bayesian reasoni...
By identifying and pursuing analogies between causal and logical in uence I show how the Bayesian ne...
First-order probabilistic models are recognized as efficient frameworks to represent several realwor...
In recent years, there has been a significant interest in integrating probability theory with first ...
Decision support systems have emerged over five decades ago to serve decision makers in uncertain co...
AbstractProbability is usually closely related to Boolean structures, i.e., Boolean algebras or prop...
We describe how to combine probabilistic logic and Bayesian networks to obtain a new frame-work ("Ba...
The Bayesian theorem was formulated in the 18th century and has been adopted as the theoretical basi...
Uncertainty is a fundamental and irreducible aspect of our knowledge about the world. Until recently...
Given the complexity of the domains for which we would like to use computers as reasoning engines, ...
Abstract. This paper presents a revised comparison of Bayesian logic programs (BLPs) and stochastic ...