Subjective Bayesian networks extend Bayesian networks by substituting the conditional probability distributions with subjective opinions. In that way they enable explicit representation of the uncertainty in the probabilistic information encoded in the network. In this paper we focus on predictive reasoning in subjective Bayesian networks and propose an inference method that is based on the operations of deduction and multiplication of subjective opinions. We demonstrate modelling and inference with subjective Bayesian networks through an example.
A Bayesian network can be used to model consisely the probabilistic knowledge with respect to a give...
Bayesianism is the position that scientific reasoning is probabilistic and that probabilities are ad...
Bayesian Belief Networks are graph-based representations of probability distributions. In the last d...
Subjective logic is a type of probabilistic logic that allows probability values to be expressed wit...
Conditional inference plays a central role in logical and Bayesian reasoning, and is used in a wide ...
Conditional inference plays a central role in logical and Bayesian reasoning, and is used in a wide ...
Qualitative and quantitative systems to deal with uncertainty coexist. Bayesian networks are a well ...
I present a framework for analyzing decision makers with an imperfect understanding of their environ...
Probabilistic Reasoning in Intelligent Systems is a complete and accessible account of the theoretic...
Expert opinion is increasingly being used to inform Bayesian Belief Networks, in particular to defin...
Abstract I present a framework for analyzing decision makers with an imperfect understanding of thei...
Probabilistic logic combines the capability of binary logic to express the structure of argument mod...
We describe how to combine probabilistic logic and Bayesian networks to obtain a new frame-work ("Ba...
Probabilistic models based on directed acyclic graphs (DAGs) have a long and rich tradition, which b...
Bayesian Belief Networks are a powerful tool for combining different knowledge sources with various ...
A Bayesian network can be used to model consisely the probabilistic knowledge with respect to a give...
Bayesianism is the position that scientific reasoning is probabilistic and that probabilities are ad...
Bayesian Belief Networks are graph-based representations of probability distributions. In the last d...
Subjective logic is a type of probabilistic logic that allows probability values to be expressed wit...
Conditional inference plays a central role in logical and Bayesian reasoning, and is used in a wide ...
Conditional inference plays a central role in logical and Bayesian reasoning, and is used in a wide ...
Qualitative and quantitative systems to deal with uncertainty coexist. Bayesian networks are a well ...
I present a framework for analyzing decision makers with an imperfect understanding of their environ...
Probabilistic Reasoning in Intelligent Systems is a complete and accessible account of the theoretic...
Expert opinion is increasingly being used to inform Bayesian Belief Networks, in particular to defin...
Abstract I present a framework for analyzing decision makers with an imperfect understanding of thei...
Probabilistic logic combines the capability of binary logic to express the structure of argument mod...
We describe how to combine probabilistic logic and Bayesian networks to obtain a new frame-work ("Ba...
Probabilistic models based on directed acyclic graphs (DAGs) have a long and rich tradition, which b...
Bayesian Belief Networks are a powerful tool for combining different knowledge sources with various ...
A Bayesian network can be used to model consisely the probabilistic knowledge with respect to a give...
Bayesianism is the position that scientific reasoning is probabilistic and that probabilities are ad...
Bayesian Belief Networks are graph-based representations of probability distributions. In the last d...