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**11 - 13**of**13**### Framework Directive through Pilot River Basins and other case studies. Fabruary 16-17, 2005.

"... usefulness of Bayesian network models ..."

### Towards a Method for Data Accuracy Assessment Utilizing a Bayesian Network Learning Algorithm

"... This research develops a data quality algorithm entitled the Accuracy Assessment Algorithm (AAA). This is an extension of research in developing an enhancement to a Bayesian Network (BN) learning algorithm called the Data Quality (DQ) algorithm. This new algorithm is concerned with estimating the ac ..."

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This research develops a data quality algorithm entitled the Accuracy Assessment Algorithm (AAA). This is an extension of research in developing an enhancement to a Bayesian Network (BN) learning algorithm called the Data Quality (DQ) algorithm. This new algorithm is concerned with estimating the accuracy levels of a dataset by assessing the quality of the data with no prior knowledge of the dataset. The AAA and associated metrics were tested using two canonical BNs and one large-scale medical network. The article presents the results regarding the efficacy of the algorithm and the implications for future research and practice.

### Study of Four Types of Learning Bayesian Networks Cases

, 2014

"... Abstract: As the combination of parameter learning and structure learning, learning Bayesian networks can also be examined, Parameter learning is estimation of the dependencies in the network. Structural learning is the estimation of the links of the network. In terms of whether the structure of the ..."

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Abstract: As the combination of parameter learning and structure learning, learning Bayesian networks can also be examined, Parameter learning is estimation of the dependencies in the network. Structural learning is the estimation of the links of the network. In terms of whether the structure of the network is known and whether the variables are all observable, there are four types of learning Bayesian networks cases. In this paper, first introduce two cases of learning Bayesian networks from complete data: known structure and unobservable variables and unknown structure and unobservable variables. Next, we study two cases of learning Bayesian networks from incomplete data: known network structure and unobservable variables, unknown network structure and unobservable variables.