Previous algorithms for the recovery of Bayesian belief network structures from data have been either highly dependent on conditional independence (CI) tests, or have required an ordering on the nodes to be supplied by the user. We present an algorithm that integrates these two approaches-CI tests are used to generate an ordering on the nodes from the database which is then used to recover the ...

Previous algorithms for the construction of Bayesian belief network structures from data have been either highly dependent on conditional independence (CI) tests, or have required an ordering on the nodes to be supplied by the user. We present an algorithm that integrates these two approaches-CI tests are used to generate an ordering on the nodes from the database which is then used to recover ...

MOTIVATION The wealth of single nucleotide polymorphism (SNP) data within candidate genes and anticipated across the genome poses enormous analytical problems for studies of genotype-to-phenotype relationships, and modern data mining methods may be particularly well suited to meet the swelling challenges. In this paper, we introduce the method of Belief (Bayesian) networks to the domain of geno...

This paper presents a scheme for large engineering project risk management using a Bayesian belief network and applies it to the Korean shipbuilding industry. Twenty-six different risks were deduced from expert interviews and a literature review. A survey analysis was conducted on 252 experts from 11 major Korean shipbuilding companies in April 2007. The overall major risks were design change, ...

Natural products and synthetic compounds are a valuable source of new small molecules leading to novel drugs to cure diseases. However identifying new biologically active small molecules is still a challenge. In this paper, we introduce a new activity prediction approach using Bayesian belief network for classification (BBNC). The roots of the network are the fragments composing a compound. The...

In this paper we propose a method for learning Bayesian belief networks from data. The method uses artificial neural networks as probability estimators, thus avoiding the need for making prior assumptions on the nature of the probability distributions governing the relationships among the participating variables. This new method has the potential for being applied to domains containing both dis...

As business environment changes dynamically, efficient management strategy and risk management are required for a company to survive. To cope with such requirements, BPMS (business process management system) has been developed. BPMS enables companies to manage and improve their processes continuously. Most of earlier studies on business process management have been focused upon process modeling...

This paper presents an efficient algorithm for constructing Bayesian belief networks from databases. The algorithm takes a database and an attributes ordering (i.e., the causal attributes of an attribute should appear earlier in the order) as input and constructs a belief network structure as output. The construction process is based on the computation of mutual information of attribute pairs. ...

With the large number of clinical practice guidelines available, there is an increasing need for a comprehensive unified model for acute ischemic stroke treatment to assist in clinical decision making. We present a unified treatment model derived through review of existing clinical practice guidelines, meta-analyses, and clinical trials. Using logic from the treatment model, a Bayesian belief n...