We replace strong independence in credal networks with the weaker notion of epistemic irrelevance. Focusing on directed trees, we show how to combine local credal sets into a global model, and we use this to construct and justify an exact message-passing algorithm that computes updated beliefs for a variable in the tree. The algorithm, which is essentially linear in the number of nodes, is formulated entirely in terms of coherent lower previsions. We supply examples of the algorithm's operation, and report an application to on-line character recognition that illustrates the advantages of our model for prediction
AbstractCredal networks relax the precise probability requirement of Bayesian networks, enabling a r...
This paper presents a family of algorithms for approximate inference in credal net-works (that is, m...
AbstractCredal networks are models that extend Bayesian nets to deal with imprecision in probability...
We replace strong independence in credal networks with the weaker notion of epistemic irrelevance. F...
AbstractWe focus on credal nets, which are graphical models that generalise Bayesian nets to impreci...
Credal networks are graph-based statistical models whose parameters take values in a set, instead of...
A credal network under epistemic irrelevance is a generalised version of a Bayesian network that loo...
\u3cp\u3eCredal networks are graph-based statistical models whose parameters take values in a set, i...
AbstractThis paper presents a complete theory of credal networks, structures that associate convex s...
We summarise and provide pointers to recent advances in inference and identification for specific ty...
We present a new approach to credal networks, which are graphical models that generalise Bayesian ne...
We replace strong independence in credal networks with the weaker notion of epistemic irrelevance. F...
We generalise Cozman’s concept of a credal network under epistemic irrelevance (2000) to the case wh...
Abstract Credal networks enhance robustness and modelling power of Bayesian networks by allowing for...
AbstractThis paper investigates the computation of lower/upper expectations that must cohere with a ...
AbstractCredal networks relax the precise probability requirement of Bayesian networks, enabling a r...
This paper presents a family of algorithms for approximate inference in credal net-works (that is, m...
AbstractCredal networks are models that extend Bayesian nets to deal with imprecision in probability...
We replace strong independence in credal networks with the weaker notion of epistemic irrelevance. F...
AbstractWe focus on credal nets, which are graphical models that generalise Bayesian nets to impreci...
Credal networks are graph-based statistical models whose parameters take values in a set, instead of...
A credal network under epistemic irrelevance is a generalised version of a Bayesian network that loo...
\u3cp\u3eCredal networks are graph-based statistical models whose parameters take values in a set, i...
AbstractThis paper presents a complete theory of credal networks, structures that associate convex s...
We summarise and provide pointers to recent advances in inference and identification for specific ty...
We present a new approach to credal networks, which are graphical models that generalise Bayesian ne...
We replace strong independence in credal networks with the weaker notion of epistemic irrelevance. F...
We generalise Cozman’s concept of a credal network under epistemic irrelevance (2000) to the case wh...
Abstract Credal networks enhance robustness and modelling power of Bayesian networks by allowing for...
AbstractThis paper investigates the computation of lower/upper expectations that must cohere with a ...
AbstractCredal networks relax the precise probability requirement of Bayesian networks, enabling a r...
This paper presents a family of algorithms for approximate inference in credal net-works (that is, m...
AbstractCredal networks are models that extend Bayesian nets to deal with imprecision in probability...