Building large-scale Bayesian networks

Climate and land-use changes are posing increasing threats to freshwater-related ecosystem services, acting both on the supply and demand sides. These changes disrupt critical processes such as nutrient cycling, sediment transport, and water flow regulation, leading to declining water quality and reduced ecosystem resilience. There is an urgent need for a deeper understanding of the dynamics of these threats, which can help enhance water management, environmental protection, and human well-being. To effectively tackle these risks, it is essential to quantitatively combine physical hazards and vulnerabilities by pinpointing hotspots where multiple stressors greatly increase the risk of water quality degradation. In response to this challenge, Bayesian network models offer a promising decision-support tool for evaluating adaptation options for water resource management, as they can integrate both quantitative and qualitative data. Building upon this approach, we developed a Spatial Bayesian Network (SBN) model to predict the probability of potential risks to water quality at the river basin scale in Italy and support the goal of achieving good chemical and ecological status according to the Water Framework Directive. This integrated model incorporates the complex relationships between land use change, climate change indicators (e.g., flood and drought intensity), and their combined impacts on water degradation. First, the baseline model uses historical patterns of climate change metrics from the CMCC DSS dataset and land use indicators from the Corine Land Cover as inputs to generate probabilistic predictions of potential risks to water quality. Then, the relationships between these variables are captured in their conditional probabilities, allowing for quantifying interactions and identifying key stressors, paving the way for scenario analysis. Finally, different future scenarios will be developed to predict the changes in water quality, considering projected climate data and socio-economic conditions. The outcome of this analysis contributes to developing an integrated management strategy that will help water managers make decisions and ultimately improve the resilience of freshwater ecosystems while supporting the implementation of adaptation strategies to address such problems.Keywords: Water Framework Directive, Water quality, Climate change, Land-use/ land-cover change Bayesian Network (BN) model

The intermittence and fluctuation of renewable energy bring significant uncertainty to the power system, which enormously increases the operational risks of the power system. The development of efficient interval prediction models can provide data support for decision making and help improve the economy and reliability of energy interconnection operation. The performance of Bayesian deep learning models and Bayesian shallow neural networks in short-term interval prediction of photovoltaic power is compared in this study. Specifically, an LSTM Approximate Bayesian Neural Network model (ABNN-I) is built on the basis of the deep learning and Monte Carlo Dropout method. Meanwhile, a Feedforward Bayesian Neural Network (ABNN-II) model is introduced by Feedforward Neural Network and the Markov Chain Monte Carlo method. To better compare and verify the interval prediction capability of the ABNN models, a novel clustering method with three-dimensional features which include the number of peaks and valleys, the average power value, and the non-stationary measurement coefficient is proposed for generating sunny and non-sunny clustering sets, respectively. Results show that the ABNN-I model has an excellent performance in the field of photovoltaic short-term interval forecasting. At a 95% confidence level, the interval coverage from ABNN-I to ABNN-II can be increased by up to 3.1% and the average width of the interval can be reduced by 56%. Therefore, with the help of the high computational capacity of deep learning and the inherent ability to quantify uncertainty of the interval forecast from Bayesian methods, this research provides high-quality interval prediction results for photovoltaic power prediction and solves the problem of difficult modeling for over-fitting that exists in the training process, especially on the non-sunny clustering sets.

H-CORE: Enabling genome-scale Bayesian analysis of biological systems without prior knowledge

Previous work has proposed the ontology-based semi-automatic generation of antipattern Bayesian Network(BN) models. The generated BN model can be used to illustrate the effects of uncertainty on antipatterns using Bayesian propagation. This can guide users in detecting particular antipattern attributes of importance based on uncertain ontological information. However, the proposed approach has been implemented in the Protege ontology editor environment and requires human intervention to specify how the BN model will be generated. The fully automated generation of ontology-based antipattern BN models still remains an open issue. SPARSE is an OWL ontology based intelligent system that assists software project managers in the antipattern detection process. In this paper, we propose the use of the resulting detected antipatterns of SPARSE, their attributes (i.e. causes, symptoms, consequences) and the ontological relationships between these attributes, in order to automatically generate BN models of the detected antipatterns. We illustrate how this approach can be implemented using an example of 8 antipattern attributes of 6 inter-related antipatterns detected using SPARSE.

Presents an efficient technique for mapping arbitrarily large Bayesian belief networks on hypercubes with deadlock-free implementation. We show that the speedup does not vary with the number of nodes in the Bayesian network and is limited by the height of the Peot-Shachter tree which is obtained by hanging the Bayesian polytree by a pivot node. We also found that the overhead in implementing Bayesian networks on parallel machines like hypercubes can be large because of the communication intensive nature of the network.

Minimum training sample size requirements for achieving high prediction accuracy with the BN model: A case study regarding seismic liquefaction

Graph Neural Networks (GNNs) is an important branch of deep learning in graph structure. As a model that can reveal deep topological information, GNNs has been widely used in various learning tasks, including physical system, protein interface prediction, disease classification, learning molecular fingerprints and so on. However, in most of these tasks, the graph data we are working with may be noisy and may contain spurious edges. That is to say, there is a lot of uncertainty associated with the underlying graph structure. The method of modeling uncertainty is to use Bayesian framework, in which graph is regarded as random variable. Introducing Bayesian framework into graph-based model, especially for semi-supervised node classification, has been shown that it can produce higher classification accuracy. In this paper, some GNNs models and Bayesian neural networks are introduced to better understand how GNNs are combined with Bayesian. Then, the development of Bayesian graph neural networks(BGNNs) in recent years is summarized, and its application in the field of engineering technology is demonstrated. Finally, the future development of BGNNs is prospected and the full text is summarized.

A method for obtaining the Bayesian network (BN) representation of a sensor's measurement process is developed so that the problems of sensor fusion and management can be approached from a unified point of view. Uncertainty, reliability, and causal information embedded in the sensor data are used to build the BN model of a sensor. The method is applied to model ground-penetrating radar, electromagnetic induction, and infrared sensors for humanitarian demining. Structural and parameter learning algorithms are employed to encode relationships among mine features, sensor measurements, and environmental conditions in the BN model. Inference is used to estimate target features in the presence of heterogeneous soil and varying environmental conditions. A multisensor fusion technique operating on BN models is developed to exploit the complementarity of the sensor measurements. Through the same approach, a BN classifier is obtained to estimate the target typology. The BN models and classifier also compute so-called confidence levels that quantify the uncertainty associated with the feature estimates and the classification decisions. The effectiveness of the approach is demonstrated by implementing these BN tools for the detection and classification of metal and plastic landmines that are characterized by different shape, size, depth, and metal content. Through BN fusion, the accuracy of the feature estimates is improved by up to 64% with respect to single-sensor measurements, and the number of objects that are both detected and classified is increased by up to 62%.

Survival prediction for gallbladder carcinoma after curative resection: Comparison of nomogram and Bayesian network models

ObjectiveThis study aims to identify relevant risk factors, assess the interactions between variables, and establish a predictive model for ischemic stroke (IS) in patients with cardiac myxoma (CM) using the Bayesian network (BN) approach.MethodsData of patients with CM were collected from three tertiary comprehensive hospitals in Beijing from January 2002 to January 2022. Age, sex, medical history, and information related to CM were extracted from the electronic medical record system. The BN model was constructed using the tabu search algorithm, and the conditional probability of each node was calculated using the maximum likelihood estimation method. The probability of each node of the network and the interrelationship between IS and its related factors were qualitatively and quantitatively analyzed. A receiver operating characteristic (ROC) curve was also plotted. Sensitivity, specificity, and area under the curve (AUC) values were calculated and compared between the BN and logistic regression models to evaluate the efficiency of the predictive model.ResultsA total of 416 patients with CM were enrolled in this study, including 61 with and 355 without IS. The BN model found that cardiac symptoms, systemic embolic symptoms, platelet counts, and tumor with high mobility were directly associated with the occurrence of IS in patients with CM. The BN model for predicting CM-IS achieved higher scores on AUC {0.706 [95% confidence interval (CI), 0.639–0.773]} vs. [0.697 (95% CI, 0.629–0.766)] and sensitivity (99.44% vs. 98.87%), but lower scores on accuracies (85.82% vs. 86.06%) and specificity (6.56% vs. 11.48%) than the logistic regression model.ConclusionCardiac symptoms, systemic embolic symptoms, platelet counts, and tumor with high mobility are candidate predictors of IS in patients with CM. The BN model was superior or at least non-inferior to the traditional logistic regression model, and hence is potentially useful for early IS detection, diagnosis, and prevention in clinical practice.

Stroke is a major chronic non-communicable disease with high incidence, high mortality, and high recurrence. To comprehensively digest its risk factors and take some relevant measures to lower its prevalence is of great significance. This study aimed to employ Bayesian Network (BN) model with Max-Min Hill-Climbing (MMHC) algorithm to explore the risk factors for stroke. From April 2019 to November 2019, Shanxi Provincial People's Hospital conducted opportunistic screening for stroke in ten rural areas in Shanxi Province. First, we employed propensity score matching (PSM) for class balancing for stroke. Afterwards, we used Chi-square testing and Logistic regression model to conduct a preliminary analysis of risk factors for stroke. Statistically significant variables were incorporated into BN model construction. BN structure learning was achieved using MMHC algorithm, and its parameter learning was achieved with Maximum Likelihood Estimation. After PSM, 748 non-stroke cases and 748 stroke cases were included in this study. BN was built with 10 nodes and 12 directed edges. The results suggested that age, fasting plasma glucose, systolic blood pressure, and family history of stroke constitute direct risk factors for stroke, whereas sex, educational levels, high density lipoprotein cholesterol, diastolic blood pressure, and urinary albumin-to-creatinine ratio represent indirect risk factors for stroke. BN model with MMHC algorithm not only allows for a complicated network relationship between risk factors and stroke, but also could achieve stroke risk prediction through Bayesian reasoning, outshining traditional Logistic regression model. This study suggests that BN model boasts great prospects in risk factor detection for stroke.

Bayesian networks (BNs) are a mature technology now widely used for modelling complex domains requiring decision making under certainty, such as environmental modelling. Object-oriented BNs (OOBNs) have been proposed to help manage the modelling complexity through structured decomposition, abstraction and encapsulation. OOBNs have been applied previously to water catchment management, but without explicit spatial modelling. In this paper, we present a novel schema that captures the spatial relationships between connected dams, as well as the temporal dynamics of the catchment over successive seasons. This is validated on an abstracted 5 dam example, with results presented for two representative cases.

Multisource geospatial data contains a lot of information that can be used for environment assessment and management. In this paper, four environmental indicators representing typical human activities in Yellow River Delta, China are extracted from multisource geospatial data. By analysing the causal relationship between these human-related indicators and NDVI, a Bayesian network (BN) model is developed. Part of the raster data pre-processed using GIS is used for training the BN model, and the other data is used for model testing. Sensitivity analysis and performance assessment showed that the BN model was good enough to reveal the impacts of human activities on land vegetation. With the trained BN model, the vegetation change under three different scenarios was also predicted. The results showed that multisource geospatial data could be successfully collated using the GIS-BN framework for vegetation detection.

Error handling in multimodal voice-enabled interfaces of tour-guide robots using graphical models

A Hybrid Modelling Framework for Ecosystem-Based Climate Change Adaptation Using System Dynamics and Bayesian Networks