There are several formalisms that enhance Bayesian networks by including relations amongst individuals as modeling primitives. For instance, Probabilistic Relational Models (PRMs) use diagrams and relational databases to represent repetitive Bayesian networks, while Relational Bayesian Networks (RBNs) employ first-order probability formulas with the same purpose. We examine the coherence checking problem for those formalisms; that is, the problem of guaranteeing that any grounding of a well-formed set of sentences does produce a valid Bayesian network. This is a novel version of de Finetti’s problem of coherence checking for probabilistic assessments. We show how to reduce the coherence checking problem in relational Bayesian networks to a ...
We study probabilistic logic under the viewpoint of the coherence principle of de Finetti. In detail...
AbstractWe study the consistency of a number of probability distributions, which are allowed to be i...
AbstractThis paper addresses the issues of knowledge representation and reasoning in large, complex,...
There are several formalisms that enhance Bayesian networks by including relations amongst individua...
Abstract. Probabilistic reasoning in Bayesian networks is normally conducted on a junction tree by r...
Relational Bayesian networks extend standard Bayesian networks by integrating some of the expressive...
This paper investigates a representation language with flexibility inspired by probabilistic logic a...
A number of representation systems have been proposed that extend the purely propositional Bayesian ...
The implication problem is to test whether a given set of independencies logically implies another i...
Bayesian networks provide an elegant formalism for representing and reasoning about uncertainty usin...
We construct a probabilistic coherence measure for information sets which determines a partial coher...
A new method is developed to represent probabilistic relations on multiple random events. Where prev...
One of the goals of artificial intelligence is to develop agents that learn and act in complex envir...
We examine the representation of judgements of stochastic independence in probabilistic logics. We f...
We study probabilistic logic under the viewpoint of the coherence principle of de Finetti. In detail...
We study probabilistic logic under the viewpoint of the coherence principle of de Finetti. In detail...
AbstractWe study the consistency of a number of probability distributions, which are allowed to be i...
AbstractThis paper addresses the issues of knowledge representation and reasoning in large, complex,...
There are several formalisms that enhance Bayesian networks by including relations amongst individua...
Abstract. Probabilistic reasoning in Bayesian networks is normally conducted on a junction tree by r...
Relational Bayesian networks extend standard Bayesian networks by integrating some of the expressive...
This paper investigates a representation language with flexibility inspired by probabilistic logic a...
A number of representation systems have been proposed that extend the purely propositional Bayesian ...
The implication problem is to test whether a given set of independencies logically implies another i...
Bayesian networks provide an elegant formalism for representing and reasoning about uncertainty usin...
We construct a probabilistic coherence measure for information sets which determines a partial coher...
A new method is developed to represent probabilistic relations on multiple random events. Where prev...
One of the goals of artificial intelligence is to develop agents that learn and act in complex envir...
We examine the representation of judgements of stochastic independence in probabilistic logics. We f...
We study probabilistic logic under the viewpoint of the coherence principle of de Finetti. In detail...
We study probabilistic logic under the viewpoint of the coherence principle of de Finetti. In detail...
AbstractWe study the consistency of a number of probability distributions, which are allowed to be i...
AbstractThis paper addresses the issues of knowledge representation and reasoning in large, complex,...