Learning accurate Bayesian networks (BNs) is a key challenge in real-world applications, es-pecially when training data are hard to acquire. Two approaches have been used to address this challenge: 1) introducing expert judgements and 2) transferring knowledge from related domains. This is the first paper to present a generic frame-work that combines both approaches to improve BN parameter learning. This framework is built upon an extended multinomial parameter learn-ing model, that itself is an auxiliary BN. It serves to integrate both knowledge transfer and expert constraints. Experimental results demonstrate improved accuracy of the new method on a va-riety of benchmark BNs, showing its potential to benefit many real-world problems.
Abstract. In Bayesian networks, prior knowledge has been used in the form of causal independencies b...
Probabilistic reasoning, among methodologies used within the domain of artificial intelligence, is r...
Algorithms for learning Bayesian networks (BNs) behave as a black box that takes a database as an in...
Learning accurate Bayesian networks (BNs) is a key challenge in real-world applications, es-pecially...
Learning accurate Bayesian networks (BNs) is a key challenge in real-world applications, especially ...
PhDOne of the hardest challenges in building a realistic Bayesian network (BN) model is to construc...
The task of learning models for many real-world problems requires incorporating domain knowledge in...
One of the essential problems on Bayesian networks (BNs) is parameter learning. When purely data-dri...
Abstract. Lack of relevant data is a major challenge for learning Bayesi-an networks (BNs) in real-w...
Building accurate models from a small amount of available training data can sometimes prove to be a ...
Most documented Bayesian network (BN) applications have been built through knowledge elicitation fro...
This work is supported by the European Research Council (ERC-2013-AdG339182-BAYES-KNOWLEDGE) and the...
Bayesian network approach to multinomial parameter learning using data and expert judgment
We examine a graphical representation of uncertain knowledge called a Bayesian network. The represen...
\u3cp\u3eThis paper describes a new approach to unify constraints on parameters with training data t...
Abstract. In Bayesian networks, prior knowledge has been used in the form of causal independencies b...
Probabilistic reasoning, among methodologies used within the domain of artificial intelligence, is r...
Algorithms for learning Bayesian networks (BNs) behave as a black box that takes a database as an in...
Learning accurate Bayesian networks (BNs) is a key challenge in real-world applications, es-pecially...
Learning accurate Bayesian networks (BNs) is a key challenge in real-world applications, especially ...
PhDOne of the hardest challenges in building a realistic Bayesian network (BN) model is to construc...
The task of learning models for many real-world problems requires incorporating domain knowledge in...
One of the essential problems on Bayesian networks (BNs) is parameter learning. When purely data-dri...
Abstract. Lack of relevant data is a major challenge for learning Bayesi-an networks (BNs) in real-w...
Building accurate models from a small amount of available training data can sometimes prove to be a ...
Most documented Bayesian network (BN) applications have been built through knowledge elicitation fro...
This work is supported by the European Research Council (ERC-2013-AdG339182-BAYES-KNOWLEDGE) and the...
Bayesian network approach to multinomial parameter learning using data and expert judgment
We examine a graphical representation of uncertain knowledge called a Bayesian network. The represen...
\u3cp\u3eThis paper describes a new approach to unify constraints on parameters with training data t...
Abstract. In Bayesian networks, prior knowledge has been used in the form of causal independencies b...
Probabilistic reasoning, among methodologies used within the domain of artificial intelligence, is r...
Algorithms for learning Bayesian networks (BNs) behave as a black box that takes a database as an in...