Add to ORKGIn this paper we show how a user can influence recovery of Bayesian Networks from a database by specifying prior knowledge. The main novelty of our approach is that the user only has to provide partial prior knowledge, which is then completed to a full prior over all possible network structures. This partial prior knowledge is expressed among variables in an intuitive pairwise way, which embodies the uncertainty of the user about his/her own prior knowledge. Thus, the uncertainty of the model is updated in the normal Bayesian way
Most of the approaches developed in the literature to elicit the a-priori distribution on Directed A...
Bayesian networks have become a standard technique in the representation of uncertain knowledge. Thi...
Learning Bayesian network structures from data is known to be hard, mainly because the number of can...
AbstractIn this paper we show how a user can influence recovery of Bayesian networks from a database...
In this paper we show how a user can influence recovery of Bayesian networks from a database by spec...
textabstractIn this paper we show how a user can influence recovery of Bayesian Networks from a data...
AbstractCurrent learning methods for general causal networks are basically data-driven. Exploration ...
This paper presents and evaluates an approach to Bayesian model averaging where the models are Bayes...
The application of Bayesian network based methods is increasingly popular in several research fields...
We propose a Bayesian framework for regression problems, which covers areas which are usually dealt ...
The elicitation of prior beliefs about the structure of a Bayesian Network is a formal step of full-...
Abstract. A Bayesian network is a graphical model that encodes probabilistic relationships among var...
AbstractThis paper provides algorithms that use an information-theoretic analysis to learn Bayesian ...
Abstract. Bayesian network structures are usually built using only the data and starting from an emp...
It is often desirable to show relationships between unstructured, potentially related data elements,...
Most of the approaches developed in the literature to elicit the a-priori distribution on Directed A...
Bayesian networks have become a standard technique in the representation of uncertain knowledge. Thi...
Learning Bayesian network structures from data is known to be hard, mainly because the number of can...
AbstractIn this paper we show how a user can influence recovery of Bayesian networks from a database...
In this paper we show how a user can influence recovery of Bayesian networks from a database by spec...
textabstractIn this paper we show how a user can influence recovery of Bayesian Networks from a data...
AbstractCurrent learning methods for general causal networks are basically data-driven. Exploration ...
This paper presents and evaluates an approach to Bayesian model averaging where the models are Bayes...
The application of Bayesian network based methods is increasingly popular in several research fields...
We propose a Bayesian framework for regression problems, which covers areas which are usually dealt ...
The elicitation of prior beliefs about the structure of a Bayesian Network is a formal step of full-...
Abstract. A Bayesian network is a graphical model that encodes probabilistic relationships among var...
AbstractThis paper provides algorithms that use an information-theoretic analysis to learn Bayesian ...
Abstract. Bayesian network structures are usually built using only the data and starting from an emp...
It is often desirable to show relationships between unstructured, potentially related data elements,...
Most of the approaches developed in the literature to elicit the a-priori distribution on Directed A...
Bayesian networks have become a standard technique in the representation of uncertain knowledge. Thi...
Learning Bayesian network structures from data is known to be hard, mainly because the number of can...