AbstractQualitative probabilistic networks are qualitative abstractions of probabilistic networks, summarising probabilistic influences by qualitative signs. As qualitative networks model influences at the level of variables, knowledge about probabilistic influences that hold only for specific values cannot be expressed. The results computed from a qualitative network, as a consequence, can be weaker than strictly necessary and may in fact be rather uninformative. We extend the basic formalism of qualitative probabilistic networks by providing for the inclusion of context-specific information about influences and show that exploiting this information upon reasoning has the ability to forestall unnecessarily weak results
This paper extends previous work on propagating qualitative uncertainty in networks in which a gener...
Probabilistic networks are now fairly well established as practical representations of knowl-edge fo...
This paper studies the relationship between probabilistic inference in Bayesian networks and evaluat...
Qualitative probabilistic networks represent prob-abilistic influences between variables. Due to the...
AbstractQualitative probabilistic networks were designed to overcome, to at least some extent, the q...
AbstractA qualitative probabilistic network is a graphical model of the probabilistic influences amo...
AbstractQualitative probabilistic networks (QPNs) are basically qualitative derivations of Bayesian ...
In A qualitative belief network, dependences between variables are indicated by qual-itative signs T...
A probabilistic network consists of a graphical representation (a directed graph) of the important v...
AbstractIn designing a Bayesian network for an actual problem, developers need to bridge the gap bet...
In designing a Bayesian network for an actual problem, developers need to bridge the gap between th...
A Bayesian network can be used to model consisely the probabilistic knowledge with respect to a give...
AbstractIndependence of causal influence (ICI) offer a high level starting point for the design of B...
This paper extends previous work on propagating qualitative uncertainty in networks in which a gener...
This paper presents some results concerning the qualitative behaviour of possibilistic networks. The...
This paper extends previous work on propagating qualitative uncertainty in networks in which a gener...
Probabilistic networks are now fairly well established as practical representations of knowl-edge fo...
This paper studies the relationship between probabilistic inference in Bayesian networks and evaluat...
Qualitative probabilistic networks represent prob-abilistic influences between variables. Due to the...
AbstractQualitative probabilistic networks were designed to overcome, to at least some extent, the q...
AbstractA qualitative probabilistic network is a graphical model of the probabilistic influences amo...
AbstractQualitative probabilistic networks (QPNs) are basically qualitative derivations of Bayesian ...
In A qualitative belief network, dependences between variables are indicated by qual-itative signs T...
A probabilistic network consists of a graphical representation (a directed graph) of the important v...
AbstractIn designing a Bayesian network for an actual problem, developers need to bridge the gap bet...
In designing a Bayesian network for an actual problem, developers need to bridge the gap between th...
A Bayesian network can be used to model consisely the probabilistic knowledge with respect to a give...
AbstractIndependence of causal influence (ICI) offer a high level starting point for the design of B...
This paper extends previous work on propagating qualitative uncertainty in networks in which a gener...
This paper presents some results concerning the qualitative behaviour of possibilistic networks. The...
This paper extends previous work on propagating qualitative uncertainty in networks in which a gener...
Probabilistic networks are now fairly well established as practical representations of knowl-edge fo...
This paper studies the relationship between probabilistic inference in Bayesian networks and evaluat...