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Structure learning is one of the main concerns in studies of Bayesian networks. In the present paper, we consider networks consisting of both observable and hidden nodes, and propose a method to investigate the existence of a hidden node between observable nodes, where all nodes are discrete. This corresponds to the model selection problem between the networks with and without the middle hidden node. When the network includes a hidden node, it has been known that there are singularities in the parameter space, and the Fisher information matrix is not positive definite. Then, the many conventional criteria for structure learning based on the Laplace approximation do not work. The proposed method is based on Bayesian clustering, and its asymptotic property justifies the result; the redundant labels are eliminated and the simplest structure is detected even if there are singularities.

Agent-based social simulation (ABSS) has become a popular method for simulating and visualizing a phenomenon while making it possible to decipher the system’s dynamism. When a large amount of data is used for an agent’s behavior, such as a questionnaire survey, a Bayesian network is often the preferred method for modeling an agent. Based on the data, a Bayesian network is used in ABSS. However, it is very difficult to learn the accurate structure of a Bayesian network from the raw data because there exist many variables and the search space is too wide. This study proposes a new method for obtaining an appropriate structure for a Bayesian network by using sensitivity analysis in a stepwise fashion. This method enables us to find a feature subset, which is good to explain objective variables without reducing the accuracy. A simple Bayesian network structure that maintains accuracy while indicating an agent’s behavior provides ABSS users with an intuitive understanding of the behavioral principle of an agent. To illustrate the effectiveness of the proposed method, data from a questionnaire survey about healthcare electronics was used.

Manufacturing companies can benefit from the early prediction and detection of failures to improve their product yield and reduce system faults through advanced data analytics. Whilst an abundance of data on their processing systems exist, they face difficulties in using it to gain insights to improve their systems. Bayesian networks (BNs) are considered here for diagnosing and predicting faults in a large manufacturing dataset from Bosch. Whilst BN structure learning has been performed traditionally on smaller sized data, this work demonstrates the ability to learn an appropriate BN structure for a large dataset with little information on the variables, for the first time. This paper also demonstrates a new framework for creating an appropriate probabilistic model for the Bosch dataset through the selection of statistically important variables on the response; this is then used to create a BN network which can be used to answer probabilistic queries and classify products based on changes in the sensor values in the production process.

The presence and development of many food safety risks are driven by factors within and outside the food supply chain, such as climate, economy and human behaviour. The interactions between these factors and the supply chain are complex and a system or holistic approach is needed to reveal cause-effect relationships and to be able to perform effective mitigation actions to minimise food safety risks. In this study, we demonstrate the potential of the Bayesian Network (BN) approach to identify and quantify the strength of relationships and interactions between the presence of food safety hazards as reported in Rapid Alert System for Food and Feed (RASFF) for fruits and vegetables on one hand, and climatic factors, economic and agronomic data on the other. To this end, all food safety notifications in RASFF (i.e. 3,781 notifications) on fruits and vegetables originating from India, Turkey and the Netherlands were collected for the period 2005-2015. In addition, climatic factors (e.g. temperature, precipitation), agricultural factors (e.g. pesticide use, fertilizer use) and economic factors (e.g. price, production volumes) were collected for the countries of origin of the product concurrent with the period of food safety notification in RASFF. A BN was constructed with 80% of the collected data using a machine-learning algorithm and optimised for each specific hazard category. The performance of the developed BN was determined in terms of accuracy of prediction of the hazard category in the evaluation set comprising 20% of the total data. The accuracy was high (95%) and the following factors contributed most: product category, notifying country, yearly production, number of notification, maximal residue level (MRL) ratio, country of origin, and the annual agricultural budget of a country. The assessment of the impact of interactions within the BN showed a significant interaction between the presence and level of a hazard as reported in RASFF and several drivers of change but at present, no definite conclusions can be drawn regarding the climatic factors and food safety hazards.

This paper introduces a causal Bayesian network model to study readmissions reduction for chronic obstructive pulmonary disease (COPD) patients. The model employs a Bayesian network learning method and adopts domain knowledge. Using this model, we analyze the impacts of critical variables on a patient's readmission risk by manipulation of such variables. Through this analysis, effective intervention options to reduce readmission can be identified, which can provide a quantitative tool for designing personalized interventions to reduce COPD readmissions.