Add to ORKGThis report1 presents probabilistic graphical models that are based on imprecise probabilities using a comprehensive language. In particular, the discussion is focused on credal networks and discrete domains. It describes the building blocks of credal networks, algorithms to perform inference, and discusses on complexity results and related work. The goal is to present an easy-to-follow introduction to the topic.
Most clustering and classification methods are based on the assumption that the objects to be cluste...
The aim of this chapter is twofold. In the first part we will provide a brief overview of the mathem...
International audienceMost clustering and classification methods are based on the assumption that th...
This report 1 presents probabilistic graphical models that are based on imprecise probabilities usin...
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...
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...
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 idea of graphical models is to use the language of graph theory to unify different classes of us...
This section investigates graphical modeling as a powerful framework for drawing inferences under im...
This paper investigates a representation language with flexibility inspired by probabilistic logic a...
The main topic of the doctoral thesis revolves around learning the structure of a graphical model fr...
In this paper, we review the role of probabilistic graphical models in artificial intelligence. We s...
Most clustering and classification methods are based on the assumption that the objects to be cluste...
The aim of this chapter is twofold. In the first part we will provide a brief overview of the mathem...
International audienceMost clustering and classification methods are based on the assumption that th...
This report 1 presents probabilistic graphical models that are based on imprecise probabilities usin...
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...
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...
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 idea of graphical models is to use the language of graph theory to unify different classes of us...
This section investigates graphical modeling as a powerful framework for drawing inferences under im...
This paper investigates a representation language with flexibility inspired by probabilistic logic a...
The main topic of the doctoral thesis revolves around learning the structure of a graphical model fr...
In this paper, we review the role of probabilistic graphical models in artificial intelligence. We s...
Most clustering and classification methods are based on the assumption that the objects to be cluste...
The aim of this chapter is twofold. In the first part we will provide a brief overview of the mathem...
International audienceMost clustering and classification methods are based on the assumption that th...