AbstractThis paper presents an overview of graphical models that can handle imprecision in probability values. The paper first reviews basic concepts and presents a brief historical account of the field. The main characteristics of the credal network model are then discussed, as this model has received considerable attention in the literature
Credal networks are graph-based statistical models whose parameters take values in a set, instead of...
The objective of this work is to generalize the pseudolikelihood-based inference method from ordinar...
We provide a theoretical investigation of probabilistic belief revision in complex frameworks, under...
AbstractThis paper presents an overview of graphical models that can handle imprecision in probabili...
We summarise and provide pointers to recent advances in inference and identification for specific ty...
This report 1 presents probabilistic graphical models that are based on imprecise probabilities usin...
This report1 presents probabilistic graphical models that are based on imprecise probabilities using...
Probabilistic graphical models constitute a fundamental tool for the development of intelligent sys...
AbstractThis paper presents a complete theory of credal networks, structures that associate convex s...
AbstractOften experts are incapable of providing “exact” probabilities; likewise, samples on which t...
Abstract Credal networks enhance robustness and modelling power of Bayesian networks by allowing for...
Probabilistic graphical models, such as Bayesian networks, allow representing conditional independen...
This article considers the extent to which Bayesian networks with imprecise probabilities, which are...
A credal network under epistemic irrelevance is a generalised version of a Bayesian network that loo...
The main topic of the doctoral thesis revolves around learning the structure of a graphical model fr...
Credal networks are graph-based statistical models whose parameters take values in a set, instead of...
The objective of this work is to generalize the pseudolikelihood-based inference method from ordinar...
We provide a theoretical investigation of probabilistic belief revision in complex frameworks, under...
AbstractThis paper presents an overview of graphical models that can handle imprecision in probabili...
We summarise and provide pointers to recent advances in inference and identification for specific ty...
This report 1 presents probabilistic graphical models that are based on imprecise probabilities usin...
This report1 presents probabilistic graphical models that are based on imprecise probabilities using...
Probabilistic graphical models constitute a fundamental tool for the development of intelligent sys...
AbstractThis paper presents a complete theory of credal networks, structures that associate convex s...
AbstractOften experts are incapable of providing “exact” probabilities; likewise, samples on which t...
Abstract Credal networks enhance robustness and modelling power of Bayesian networks by allowing for...
Probabilistic graphical models, such as Bayesian networks, allow representing conditional independen...
This article considers the extent to which Bayesian networks with imprecise probabilities, which are...
A credal network under epistemic irrelevance is a generalised version of a Bayesian network that loo...
The main topic of the doctoral thesis revolves around learning the structure of a graphical model fr...
Credal networks are graph-based statistical models whose parameters take values in a set, instead of...
The objective of this work is to generalize the pseudolikelihood-based inference method from ordinar...
We provide a theoretical investigation of probabilistic belief revision in complex frameworks, under...
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