In this paper we consider the problem of performing Bayesian model-averaging over a class of discrete Bayesian network structures consistent with a partial ordering and with bounded in-degree k. We show that for N nodes this class contains in the worst-case at least Ω ( �N/2�N/2 k) distinct network structures, and yet model averaging over these structures can be performed using O ( �N � k · N) operations. Furthermore we show that there exists a single Bayesian network that defines a joint distribution over the variables that is equivalent to model averaging over these structures. Although constructing this network is computationally prohibitive, we show that it can be approximated by a tractable network, allowing approximate model-averaged...
Selecting a single model for clustering ignores the uncertainty left by finite data as to which is t...
Bayesian network structure learning is often performed in a Bayesian setting, by evaluating candidat...
Bayesian network models are widely used for supervised prediction tasks such as classi cation. Usua...
A Bayesian network is a widely used probabilistic graphical model with applications in knowledge dis...
In this paper 1 we consider the problem of performing Bayesian model-averaging over a class of discr...
In this paper we develop an algorithm to find the k-best equivalence classes of Bayesian networks. O...
Background: Considerable progress has been made on algorithms for learning the structure of Bayesian...
Naive Bayes (NB) is well-known to be a simple but effective classifier, especially when combined wit...
When applied to classification problems, Bayesian networks are often used to infer a class variable ...
In many domains, we are interested in analyzing the structure of the underlying distribution, e.g., ...
This paper presents and evaluates an approach to Bayesian model averaging where the models are Bayes...
Abstract—The motivation for this paper is to apply Bayesian structure learning using Model Averaging...
In many domains, we are interested in analyzing the structure of the underlying distribution, e.g., ...
A Bayesian network (BN) is a probabilistic graphical model with applications in knowledge discovery ...
This paper presents and evaluates an approach to Bayesian model averaging where the models are Bayes...
Selecting a single model for clustering ignores the uncertainty left by finite data as to which is t...
Bayesian network structure learning is often performed in a Bayesian setting, by evaluating candidat...
Bayesian network models are widely used for supervised prediction tasks such as classi cation. Usua...
A Bayesian network is a widely used probabilistic graphical model with applications in knowledge dis...
In this paper 1 we consider the problem of performing Bayesian model-averaging over a class of discr...
In this paper we develop an algorithm to find the k-best equivalence classes of Bayesian networks. O...
Background: Considerable progress has been made on algorithms for learning the structure of Bayesian...
Naive Bayes (NB) is well-known to be a simple but effective classifier, especially when combined wit...
When applied to classification problems, Bayesian networks are often used to infer a class variable ...
In many domains, we are interested in analyzing the structure of the underlying distribution, e.g., ...
This paper presents and evaluates an approach to Bayesian model averaging where the models are Bayes...
Abstract—The motivation for this paper is to apply Bayesian structure learning using Model Averaging...
In many domains, we are interested in analyzing the structure of the underlying distribution, e.g., ...
A Bayesian network (BN) is a probabilistic graphical model with applications in knowledge discovery ...
This paper presents and evaluates an approach to Bayesian model averaging where the models are Bayes...
Selecting a single model for clustering ignores the uncertainty left by finite data as to which is t...
Bayesian network structure learning is often performed in a Bayesian setting, by evaluating candidat...
Bayesian network models are widely used for supervised prediction tasks such as classi cation. Usua...