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
This paper investigates a representation language with flexibility inspired by probabilistic logic a...
Probability theory provides a mathematically rigorous yet conceptually flexible calculus of uncertai...
This paper is a multidisciplinary review of empirical, statistical learning from a graph-ical model ...
AbstractThis paper presents an overview of graphical models that can handle imprecision in probabili...
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...
We summarise and provide pointers to recent advances in inference and identification for specific ty...
The likelihood approach to statistics can be inter-preted as a theory of fuzzy probability. This pap...
Contains fulltext : 58959.pdf (publisher's version ) (Open Access)'A graphical mod...
AbstractThis paper presents a complete theory of credal networks, structures that associate convex s...
Probabilistic graphical models, such as Bayesian networks, allow representing conditional independen...
The aim of this chapter is twofold. In the first part we will provide a brief overview of the mathem...
Probabilistic graphical models constitute a fundamental tool for the development of intelligent sys...
Probabilistic graphical models provide a natural framework for the representation of complex systems...
In this paper, we review the role of probabilistic graphical models in artificial intelligence. We s...
This paper investigates a representation language with flexibility inspired by probabilistic logic a...
Probability theory provides a mathematically rigorous yet conceptually flexible calculus of uncertai...
This paper is a multidisciplinary review of empirical, statistical learning from a graph-ical model ...
AbstractThis paper presents an overview of graphical models that can handle imprecision in probabili...
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...
We summarise and provide pointers to recent advances in inference and identification for specific ty...
The likelihood approach to statistics can be inter-preted as a theory of fuzzy probability. This pap...
Contains fulltext : 58959.pdf (publisher's version ) (Open Access)'A graphical mod...
AbstractThis paper presents a complete theory of credal networks, structures that associate convex s...
Probabilistic graphical models, such as Bayesian networks, allow representing conditional independen...
The aim of this chapter is twofold. In the first part we will provide a brief overview of the mathem...
Probabilistic graphical models constitute a fundamental tool for the development of intelligent sys...
Probabilistic graphical models provide a natural framework for the representation of complex systems...
In this paper, we review the role of probabilistic graphical models in artificial intelligence. We s...
This paper investigates a representation language with flexibility inspired by probabilistic logic a...
Probability theory provides a mathematically rigorous yet conceptually flexible calculus of uncertai...
This paper is a multidisciplinary review of empirical, statistical learning from a graph-ical model ...
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