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.

This literature review discusses different methods under the general rubric of learning Bayesian networks from data, and includes some overlapping work on more general probabilistic networks. Connections are drawn between the statistical, neural network, and uncertainty communities, and between the different methodological communities, such as Bayesian, description length, and classical statistics. Basic concepts for learning and Bayesian networks are introduced and methods are then reviewed. Methods are discussed for learning parameters of a probabilistic network, for learning the structure, and for learning hidden variables. The presentation avoids formal definitions and theorems, as these are plentiful in the literature, and instead illustrates key concepts with simplified examples.