Features Of Bayesian Networks Research Articles

A New Multi-Dimensional Framework for Start-Ups Lifespan Assessment Using Bayesian Networks

As historical data are typically unavailable for a start-up, risk assessment is always complex and challenging. Traditional methods are incapable of capturing all facets of this complexity; therefore, more sophisticated tools are necessary. Using an expert-elicited Bayesian networks (BNs) methodology, this paper aims to provide a method for combining diverse sources of information, such as historical data, expert knowledge, and the unique characteristics of each start-up, to estimate the default rate at various stages of the life cycle. The proposed method not only reduces the cognitive error of expert opinion for a new start-up but also considers the learning feature of BNs and the effect of lifespan when updating default estimations. In addition, the model considers the impact of investors’ risk appetite. Furthermore, the model can rank the most effective risk factors at various stages. The receiver operating characteristic (ROC) curve was utilized to assess the model’s explanatory power. Moreover, three distinct case studies were used to demonstrate the model’s capabilities.

Bayesian network-based missing mechanism identification (BN-MMI) method in medical research

BackgroundTraditional approaches to identify missing mechanisms are usually based on the hypothesis test and confronted with both theoretical and practical challenges. It has been proved that the Bayesian network is powerful in integrating, analyzing and visualizing information, and some previous researches have verified the promising features of Bayesian network to deal with the aforementioned challenges in missing mechanism identification. Based on the above reasons, this paper explores the application of Bayesian network to the identification of missing mechanisms for the first time, and proposes a new method, the Bayesian network-based missing mechanism identification (BN-MMI) method, to identify missing mechanism in medical research.MethodsThe procedure of BN-MMI method consists three easy-to-implement steps: estimating the missing data structure by the Bayesian network; assessing the credibility of the estimated missing data structure; and identifying the missing mechanism from the estimated missing data structure. The BN-MMI method is verified by simulation research and empirical research.ResultsThe simulation study verified the validity, consistency and robustness of BN-MMI method, and indicated its outperformance in contrast to the traditional logistic regression method. In addition, the empirical study illustrated the applicability of BN-MMI method in the real world by an example of medical record data.ConclusionsIt was confirmed that the BN-MMI method itself, together with human knowledge and expertise, could identify the missing mechanisms according to the probabilistic dependence/independence relations among variables of interest. At the same time, our research shed light upon the potential application of BN-MMI method to a broader range of missing data issues in medical studies.

A multi-layer Bayesian network method for supply chain disruption modelling in the wake of the COVID-19 pandemic

ABSTRACT While the majority of companies anticipated the negative and severe impacts of the COVID-19 pandemic on the supply chains (SC), most of them lacked guidance on how to model disruptions and their performance impacts under pandemic conditions. Lack of such guidance resulted in delayed reactions, incomplete understanding of pandemic impacts, and late deployment of recovery actions. In this study, we offer a method of modelling and quantifying the SC disruption impacts in the wake of a pandemic. We develop a multi-layer Bayesian network (BN) model that can be used to identify SC disruption triggers and risk events amid the COVID-19 pandemic and quantify the consequences of pandemic disruptions. The unique features of BN, such as forward and backward propagation analysis, are utilised to simulate and measure the impact of different triggers on SC financial performance and business continuity. In this way, we combine resilience and viability SC perspectives and explicitly account for the pandemic settings. The outcomes of this research open a novel theoretical lens on application of BNs to SC disruption modelling in the pandemic setting. Our results can be used as a decision-support tool to predict and better understand the pandemic impacts on SC performance.

Discovering novel cancer bio-markers in acquired lapatinib resistance using Bayesian methods.

Signalling transduction pathways (STPs) are commonly hijacked by many cancers for their growth and malignancy, but demystifying their underlying mechanisms is difficult. Here, we developed methodologies with a fully Bayesian approach in discovering novel driver bio-markers in aberrant STPs given high-throughput gene expression (GE) data. This project, namely 'PathTurbEr' (Pathway Perturbation Driver) uses the GE dataset derived from the lapatinib (an EGFR/HER dual inhibitor) sensitive and resistant samples from breast cancer cell lines (SKBR3). Differential expression analysis revealed 512 differentially expressed genes (DEGs) and their pathway enrichment revealed 13 highly perturbed singalling pathways in lapatinib resistance, including PI3K-AKT, Chemokine, Hippo and TGF-$\beta $ singalling pathways. Next, the aberration in TGF-$\beta $ STP was modelled as a causal Bayesian network (BN) using three MCMC sampling methods, i.e. Neighbourhood sampler (NS) and Hit-and-Run (HAR) sampler that potentially yield robust inference with lower chances of getting stuck at local optima and faster convergence compared to other state-of-art methods. Next, we examined the structural features of the optimal BN as a statistical process that generates the global structure using $p_1$-model, a special class of Exponential Random Graph Models (ERGMs), and MCMC methods for their hyper-parameter sampling. This step enabled key drivers identification that drive the aberration within the perturbed BN structure of STP, and yielded 34, 34 and 23 perturbation driver genes out of 80 constituent genes of three perturbed STP models of TGF-$\beta $ signalling inferred by NS, HAR and MH sampling methods, respectively. Functional-relevance and disease-relevance analyses suggested their significant associations with breast cancer progression/resistance.

Exploiting the Capabilities of Bayesian Networks for Engineering Risk Assessment: Causal Reasoning through Interventions.

In the last decade, Bayesian networks (BNs) have been widely used in engineering risk assessment due to the benefits that they provide over other methods. Among these, the most significant is the ability to model systems, causal factors, and their dependencies in a probabilistic manner. This capability has enabled the community to do causal reasoning through associations, which answers questions such as: “How does new evidence x′ about the occurrence of event X change my belief about the occurrence of event Y?” Associative reasoning has helped risk analysts to identify relevant risk‐contributing factors and perform scenario analysis by evidence propagation. However, engineering risk assessment has yet to explore other features of BNs, such as the ability to reason through interventions, which enables the BN model to support answering questions of the form “How does doing X=x′ change my belief about the occurrence of event Y?” In this article, we propose to expand the scope of use of BN models in engineering risk assessment to support intervention reasoning. This will provide more robust risk‐informed decision support by enabling the modeling of policies and actions before being implemented. To do this, we provide the formal mathematical background and tools to model interventions in BNs and propose a framework that enables its use in engineering risk assessment. This is demonstrated in an illustrative case study on third‐party damage of natural gas pipelines, showing how BNs can be used to inform decision‐makers about the effect that new actions/policies can have on a system.

Security Management of Process Plants by a Bayesian Network Methodology

Before the tragedy of 9/11, the perception of risk in process plants was mainly focused on accidental events caused by technical failures, human errors or natural events. However, since then, the risk of deliberate actions against process facilities - also known as security risk - has also become a concern. Security risk assessment of engineering systems and infrastructures constitutes a complex task since a significant multitude of technical and socio-political information is needed to reasonably predict the risk of intentional malevolent acts. In the present study, a methodology based on Bayesian network (BN) has been applied to increase the security of critical infrastructures via cost-effective allocation of security measures. Using the probability updating feature of BN, the proposed methodology can be employed to investigate the effect of vulnerabilities on adversaries' preferences while planning security scenarios. Moreover, the proposed methodology is capable of efficiently identifying an optimal defensive strategy given a security scenario (i.e., an attack) through maximizing defenders' expected utility.

A Bayesian network methodology for optimal security management of critical infrastructures

Security management of critical infrastructures is a complex task as a great variety of technical and socio-political information is needed to realistically predict the risk of intentional malevolent acts. In the present study, a methodology based on Limited Memory Influence Diagram (LIMID) has been developed for the protection of critical infrastructures via cost-effective allocation of security measures. LIMID is an extension of Bayesian network (BN) intended for decision-making, allowing for efficient modelling of complex systems while accounting for interdependencies and interaction of system components. The probability updating feature of BN has been used to investigate the effect of vulnerabilities on adversaries’ preferences when planning attacks. Moreover, the proposed methodology has been shown to be able to identify an optimal defensive strategy given an attack through maximizing defenders’ expected utility. Despite being demonstrated via a chemical facility, the methodology can easily be tailored to a wide variety of critical infrastructures.

Exploring the effect of a user’s personality traits on tactile communication with a robot using Bayesian networks

Because robots are physically embodied agents, touch is one of the important modalities through which robots communicate with humans. Among the several factors that affect human-robot interaction, this research focuses on the effect of a user’s personality traits on tactile interactions with a robot. Participants interacted freely with a robot and their tactile interaction patterns were analyzed. Several classifiers were used to examine the effect of a participant’s degree of extroversion on tactile communication patterns with the robot and our results showed that a user’s personality traits affected the way in which they interacted with the robot. Specifically, important features of Bayesian networks, such as the Markov blanket and what-if/goal-seeking power were tested and showed the effect of personality on tactile interaction with respect to where and how participants touched the robot. We also found that, by using Bayesian network classifiers, a user’s personality traits can be inferred based on tactile communication patterns.

A Bayesian model for real-time safety management in construction sites

Purpose This article reports on an ongoing research project, which is aimed at implementing advanced probabilistic models for real-time identification of hazardous events at construction sites. The model has intelligent capabilities for near real-time automated recognition of hazardous events during the execution phase. To achieve this, features of Bayesian Networks have been exploited. In addition, inputs to the model are assumed to be provided by a pervasive monitoring system deployed on the site. The need for this kind of intelligent tool is determined by the complexity inherent in construction sites, due to a variety of reasons, such as heterogeneity of the actors, the simultaneous nature of operations, harsh contextual conditions, and the only partially efficient current approach based on health and safety plans. Hence, this model is proposed as a support tool for health and safety coordinators for supervision of sites as they cannot guarantee a continuous physical presence. Method Given that there are no long-time series on past occurrences of hazardous events in all the potential contextual combinations presently available, the probabilistic models cannot be learned just through datasets. For that reason, the available data have been integrated with expert opinions. In particular, the conditional probabilities of the Bayesian networks are estimated by an elicitation process of subjective knowledge from the opinions of experts. The complexity of the phenomena under analysis are modelled as a tree structure with several levels (corresponding to the work-breakdown structure hierarchy), which itself is based on the top-down technique; it provides therefore a clear view of the global picture. The built-hierarchical tree allows the expert to weigh more easily causal relationships involved and also to define the qualitative structure of the net. Furthermore, the article describes and tests how conditional probabilities of the variables in the networks can be estimated, through gathering and interviewing groups of stakeholders and experts. Results & Discussion Our research has led to the definition of a probabilistic model using elicitation techniques for subjective knowledge. Furthermore, the development of such a model is part of a wider system relying on the implementation of a real-time monitoring network.

Bayesian Network Learn Method for Machine Tool Thermal Stability Modeling

Maintain the thermal stability of the machine tool is a common problem to achieve intelligent and precise processing control, and its difficulty lies in modeling and real-time compensation. In this paper, considering the correlation of various factors, the correlation of those factors according to experiment data was analysis and optimized, and a dynamic model of thermal error compensation of CNC machine tool based on Bayesian Network theory was found. Moreover, because of the self-learning feature of Bayesian network, the model can be continuously optimized by updating dynamic coefficient, and reflect the changes of processing condition. Finally, the feasibility and validation of this model were proved through the experiment.

Safety analysis in process facilities: Comparison of fault tree and Bayesian network approaches

Safety analysis in gas process facilities is necessary to prevent unwanted events that may cause catastrophic accidents. Accident scenario analysis with probability updating is the key to dynamic safety analysis. Although conventional failure assessment techniques such as fault tree (FT) have been used effectively for this purpose, they suffer severe limitations of static structure and uncertainty handling, which are of great significance in process safety analysis. Bayesian network (BN) is an alternative technique with ample potential for application in safety analysis. BNs have a strong similarity to FTs in many respects; however, the distinct advantages making them more suitable than FTs are their ability in explicitly representing the dependencies of events, updating probabilities, and coping with uncertainties. The objective of this paper is to demonstrate the application of BNs in safety analysis of process systems. The first part of the paper shows those modeling aspects that are common between FT and BN, giving preference to BN due to its ability to update probabilities. The second part is devoted to various modeling features of BN, helping to incorporate multi-state variables, dependent failures, functional uncertainty, and expert opinion which are frequently encountered in safety analysis, but cannot be considered by FT. The paper concludes that BN is a superior technique in safety analysis because of its flexible structure, allowing it to fit a wide variety of accident scenarios.

Bayesian Networks and Adaptive Management of Wildlife Habitat

Adaptive management is an iterative process of gathering new knowledge regarding a system's behavior and monitoring the ecological consequences of management actions to improve management decisions. Although the concept originated in the 1970s, it is rarely actively incorporated into ecological restoration. Bayesian networks (BNs) are emerging as efficient ecological decision-support tools well suited to adaptive management, but examples of their application in this capacity are few. We developed a BN within an adaptive-management framework that focuses on managing the effects of feral grazing and prescribed burning regimes on avian diversity within woodlands of subtropical eastern Australia. We constructed the BN with baseline data to predict bird abundance as a function of habitat structure, grazing pressure, and prescribed burning. Results of sensitivity analyses suggested that grazing pressure increased the abundance of aggressive honeyeaters, which in turn had a strong negative effect on small passerines. Management interventions to reduce pressure of feral grazing and prescribed burning were then conducted, after which we collected a second set of field data to test the response of small passerines to these measures. We used these data, which incorporated ecological changes that may have resulted from the management interventions, to validate and update the BN. The network predictions of small passerine abundance under the new habitat and management conditions were very accurate. The updated BN concluded the first iteration of adaptive management and will be used in planning the next round of management interventions. The unique belief-updating feature of BNs provides land managers with the flexibility to predict outcomes and evaluate the effectiveness of management interventions.

Requirements Analysis: Evaluating KAOS Models

Wigmore’s charts and Bayesian networks are used to represent graphically the construction of arguments and to evaluate them. KAOS is a goal oriented requirements analysis method that enables the analysts to capture requirements through the realization of the business goals. However, KAOS does not have inbuilt mechanism for evaluating these goals and the inferring process. This paper proposes a method for evaluating KAOS models through the extension of Wigmore’s model with features of Bayesian networks.