Bayesian Network Method Research Articles

A Bayesian Network Analysis of Ethical Behavior

Using one of the major domains of macromarketing – ethics – this paper aims to introduce the Bayesian network (BN) method and demonstrate its added value for macro-level decision makers. Bayesian networks are particularly important for macromarketers because they allow researchers to analyze a domain from a system perspective. The BN approach is considered one of the most powerful tools for observing system changes. The method can also deal with multiple variables at once, which can lead to efficient scenario analyses, critical for understanding how a system functions. As such, BNs offer a powerful tool for macromarketers who deal with systems, interactions, and higher levels of aggregation. We believe that the adoption of this methodology by macromarketing researchers is likely to contribute to the discipline by advancing the understanding of how certain systemic/network relationships and various domains of macromarketing work.

Breast Cancer classification using extracted parameters from a terahertz dielectric model of human breast tissue.

Our previous study proposed a dielectric model for human breast tissue and provided initial analysis of classification potential of the eight model parameters and their multiparameter combinations with the support vector machine (SVM). A combination of three model parameters could achieve a leave-one-out cross validation accuracy of 93.2%. However, the SVM approach fails to exploit the combinations of more than three model parameters for classification improvement. Thus, the Bayesian neural network (BNN) method is employed to overcome this problem based on its advantages of handling our small data and high complexity of the multiparamter combinations. The BNN successfully classifies the data using the combinations of four model parameters with an accuracy, estimated by leave-one-out cross validation, of 97.3%. Overall performance assessed by leaveone-out and repeated random-subsampling cross validations for all examined combinations is also remarkably improved by BNN. The results indicate the advance of BNN as compared to SVM in utilising the model parameters for detecting tumour from normal breast tissue.

Bayesian networks for enhancement of requirements engineering: a literature review

Requirements analysis is the software engineering stage that is closest to the users' world. It also involves tasks that are knowledge intensive. Thus, the use of Bayesian networks (BNs) to model this knowledge would be a valuable aid. These probabilistic models could manage the imprecision and ambiguities usually present in requirements engineering (RE). In this work, we conduct a literature review focusing on where and how BNs are applied on subareas of RE in order to identify which gaps remain uncovered and which methods might engineers employ to incorporate this intelligent technique into their own requirements processes. The scarcity of identified studies (there are only 20) suggests that not all RE areas have been properly investigated in the literature. The evidence available for adopting BNs into RE is sufficiently mature yet the methods applied are not easily translatable to other topics. Nonetheless, there are enough studies supporting the applicability of synergistic cooperation between RE and BNs. This work provides a background for understanding the current state of research encompassing RE and BNs. Functional, non-functional and -ilities requirements artifacts are enhanced by the use of BNs. These models were obtained by interacting with experts or by learning from databases. The most common criticism from the point of view of BN experts is that the models lack validation, whereas requirements engineers point to the lack of a clear application method for BNs and the lack of tools for incorporating them as built-in help functions.

Late customization strategy with service levels requirements

Both supply issues and interaction between various stages of a production network are common occurrences in product customization under a delayed differentiation strategy. This paper studies non-decouple systems for product customization in the context of a single-market segment under a delayed differentiation strategy by incorporating supply issues and interaction between various stages of a production network. We analyze two alternatives for customizing two individual products. One alternative is the contractual obligations for raw materials delivery where the supplier is responsible for maintaining certain inventory service levels agreements. The other alternative is the customer demand requirements where the manufacturer is responsible for maintaining certain end-products service levels to prevent customer erosion. We characterize the service level requirements in both scenarios and determine the optimal customization point of the production network where the practitioner can operate at minimum cost. We use a Bayesian Belief Networks method to model interaction between stages of the production network and derive the inventory service level requirements. A mini-case involving the customization of a personal desktop computer is used to illustrate the applicability of this framework. We also compare the benefits of delayed product differentiation under non-decouple systems and the traditional decouple systems and provide insights into how firms can choose the right strategy to effectively compete.

A multinomial logit model-Bayesian network hybrid approach for driver injury severity analyses in rear-end crashes

Rear-end crash is one of the most common types of traffic crashes in the U.S. A good understanding of its characteristics and contributing factors is of practical importance. Previously, both multinomial Logit models and Bayesian network methods have been used in crash modeling and analysis, respectively, although each of them has its own application restrictions and limitations. In this study, a hybrid approach is developed to combine multinomial logit models and Bayesian network methods for comprehensively analyzing driver injury severities in rear-end crashes based on state-wide crash data collected in New Mexico from 2010 to 2011. A multinomial logit model is developed to investigate and identify significant contributing factors for rear-end crash driver injury severities classified into three categories: no injury, injury, and fatality. Then, the identified significant factors are utilized to establish a Bayesian network to explicitly formulate statistical associations between injury severity outcomes and explanatory attributes, including driver behavior, demographic features, vehicle factors, geometric and environmental characteristics, etc. The test results demonstrate that the proposed hybrid approach performs reasonably well. The Bayesian network reference analyses indicate that the factors including truck-involvement, inferior lighting conditions, windy weather conditions, the number of vehicles involved, etc. could significantly increase driver injury severities in rear-end crashes. The developed methodology and estimation results provide insights for developing effective countermeasures to reduce rear-end crash injury severities and improve traffic system safety performance.

Bayesian graphical network analyses reveal complex biological interactions specific to Alzheimer's disease.

With different approaches to finding prognostic or diagnostic biomarkers for Alzheimer's disease (AD), many studies pursue only brief lists of biomarkers or disease specific pathways, potentially dismissing information from groups of correlated biomarkers. Using a novel Bayesian graphical network method, with data from the Australian Imaging, Biomarkers and Lifestyle (AIBL) study of aging, the aim of this study was to assess the biological connectivity between AD associated blood-based proteins. Briefly, three groups of protein markers (18, 37, and 48 proteins, respectively) were assessed for the posterior probability of biological connection both within and between clinical classifications. Clinical classification was defined in four groups: high performance healthy controls (hpHC), healthy controls (HC), participants with mild cognitive impairment (MCI), and participants with AD. Using the smaller group of proteins, posterior probabilities of network similarity between clinical classifications were very high, indicating no difference in biological connections between groups. Increasing the number of proteins increased the capacity to separate both hpHC and HC apart from the AD group (0 for complete separation, 1 for complete similarity), with posterior probabilities shifting from 0.89 for the 18 protein group, through to 0.54 for the 37 protein group, and finally 0.28 for the 48 protein group. Using this approach, we identified beta-2 microglobulin (β2M) as a potential master regulator of multiple proteins across all classifications, demonstrating that this approach can be used across many data sets to identify novel insights into diseases like AD.

A New Bayesian Network Method for Computing Bottom Event's Structural Importance Degree using Jointree

T Bayesian network methodology taking place of fault tree analysis used for reliability assessment has gotten lots of attention in recent years. On basis of the current Bayesian network method used for calculating structural importance degree, a new Bayesian network method is raised. This new method can avoid repeat modification of the parameters in the conditional probability tables, and hence the intermediate results computed during inference process can be shared to decrease calculating complexity. The new method is proved to be correct in a mathematic way and a corresponding algorithm named SID_Jointree for realizing this new method is designed, which guarantees the new method can be realized in computer. Finally, the correctness and efficiency of the new method is validated by using two fault tree cases.

Predicting the functions of long noncoding RNAs using RNA-seq based on Bayesian network.

Long noncoding RNAs (lncRNAs) have been shown to play key roles in various biological processes. However, functions of most lncRNAs are poorly characterized. Here, we represent a framework to predict functions of lncRNAs through construction of a regulatory network between lncRNAs and protein-coding genes. Using RNA-seq data, the transcript profiles of lncRNAs and protein-coding genes are constructed. Using the Bayesian network method, a regulatory network, which implies dependency relations between lncRNAs and protein-coding genes, was built. In combining protein interaction network, highly connected coding genes linked by a given lncRNA were subsequently used to predict functions of the lncRNA through functional enrichment. Application of our method to prostate RNA-seq data showed that 762 lncRNAs in the constructed regulatory network were assigned functions. We found that lncRNAs are involved in diverse biological processes, such as tissue development or embryo development (e.g., nervous system development and mesoderm development). By comparison with functions inferred using the neighboring gene-based method and functions determined using lncRNA knockdown experiments, our method can provide comparable predicted functions of lncRNAs. Overall, our method can be applied to emerging RNA-seq data, which will help researchers identify complex relations between lncRNAs and coding genes and reveal important functions of lncRNAs.

Multi-state system importance analysis method of fuzzy Bayesian networks

In order to quantify the reliability index of the real system and identify the key events affecting the reliability of the system, fuzzy importance analysis method which can be applied to multi-state system is proposed on the basis of Bayesian network targeting the fuzziness and uncertainties of information. The fuzzy set theory is introduced into the Bayesian network analysis. The failure likelihood of the various components of the system is represented by fuzzy subset, and the fault states of components and system are described by fuzzy numbers. Considering the uncertainty of the fault logical relationship among components, the fuzzy conditional probability tables are used to describe the fault logical relationship among components. Two kinds of fuzzy Bayesian network importance are proposed on the basis of fuzzy Bayesian network analysis algorithms, which describe the contribution of various components to system failure clearly. At last, it is proved that the methods are feasible in the important analysis of the accident of crane rope-breaking.

Risk Assessment of Multi-State Bayesian Network in an Oil Gathering and Transferring System

The research objects of conventional risk assessment methods such as fault tree, event tree, etc., are always considered as two ultimate states, one is the complete failure and the other is the perfect state. In this condition, the effect of partial system failure on system performance is not taken into account, which leads to a big difference between calculation results by risk assessment model and actual situation. Multi-state Markov model has been proven to be a versatile tool for modeling multi-state system. It has been effectively used to interpret the degradation relationship between the perfect state and complete failure in the real world. Unfortunately, multi-state Markov model suffers state space explosion, where the generating states grows exponentially with the number of components comprised in the system. For these issues, a multi-state Bayesian network method and its corresponding algorithms for risk assessment were proposed in this paper. In addition, the availability and accuracy of the proposed method were validated by an application example of the oil gathering and transferring system. The results showed that multi-state Bayesian network method was effective to cope with multi-state system by its powerful ability of bidirectional reasoning and probability updating. Compared with multi-state Markov model, the multi-state Bayesian network method is simplified and its computational efficiency is improved. Furthermore, this method could ensure the credibility of risk assessment results as far as possible by taking new information into account and updating the prior beliefs about the accident.

On Pass-through of RMB Exchange Rate to Prices of Different Industries

Abstract This paper employs time series method and Dynamic Bayesian Network method to measure the pass through of RMB exchange rate to prices of different industries. The result shows that the direct transfer effect of RMB exchange rate on RMPI, CGPI and CPI is remarkable. At the same time, the RMB exchange rate can also have indirect influence on PPI, CGPI and CPI of different industries. This study indicates that wood, chemistry, textile and other labor-intensive industry prices are sensitive to the fluctuation of RMB exchange rate, while the exchange rate pass-through effects of energy, black metal, non-ferrous metals and other mineral industries prices are not significant.

Variables Sequence Calculating Method in the Process of Bayesian Network Constructing for Battlefield Damage Diagnosis

In order to improve damage diagnosis ability of maintenance personnel, constructing method of Bayesian network applied to weapon battlefield damage diagnosis is researched. Battlefield damage correlations among damaged parts of weapon are analyzed if one weapon is attacked by bombshells, and is the basis of damage diagnosis with the use of Bayesian network. Bayesian network for damage diagnosis is constructed based on K2 arithmetic. Variables sequence is the key factor of Bayesian network constructing, a statistical method of ascertaining variables sequence is presented with the use of weapon battlefield simulation technology.

Effect of reporting bias in the analysis of spontaneous reporting data.

It is well-known that a spontaneous reporting system suffers from significant under-reporting of adverse drug reactions from the source population. The existing methods do not adjust for such under-reporting for the calculation of measures of association between a drug and the adverse drug reaction under study. Often there is direct and/or indirect information on the reporting probabilities. This work incorporates the reporting probabilities into existing methodologies, specifically to Bayesian confidence propagation neural network and DuMouchel's empirical Bayesian methods, and shows how the two methods lead to biased results in the presence of under-reporting. Considering all the cases to be reported, the association measure for the source population can be estimated by using only exposure information through a reference sample from the source population.

FamSeq: a variant calling program for family-based sequencing data using graphics processing units.

Various algorithms have been developed for variant calling using next-generation sequencing data, and various methods have been applied to reduce the associated false positive and false negative rates. Few variant calling programs, however, utilize the pedigree information when the family-based sequencing data are available. Here, we present a program, FamSeq, which reduces both false positive and false negative rates by incorporating the pedigree information from the Mendelian genetic model into variant calling. To accommodate variations in data complexity, FamSeq consists of four distinct implementations of the Mendelian genetic model: the Bayesian network algorithm, a graphics processing unit version of the Bayesian network algorithm, the Elston-Stewart algorithm and the Markov chain Monte Carlo algorithm. To make the software efficient and applicable to large families, we parallelized the Bayesian network algorithm that copes with pedigrees with inbreeding loops without losing calculation precision on an NVIDIA graphics processing unit. In order to compare the difference in the four methods, we applied FamSeq to pedigree sequencing data with family sizes that varied from 7 to 12. When there is no inbreeding loop in the pedigree, the Elston-Stewart algorithm gives analytical results in a short time. If there are inbreeding loops in the pedigree, we recommend the Bayesian network method, which provides exact answers. To improve the computing speed of the Bayesian network method, we parallelized the computation on a graphics processing unit. This allowed the Bayesian network method to process the whole genome sequencing data of a family of 12 individuals within two days, which was a 10-fold time reduction compared to the time required for this computation on a central processing unit.

MACOED: a multi-objective ant colony optimization algorithm for SNP epistasis detection in genome-wide association studies.

The existing methods for genetic-interaction detection in genome-wide association studies are designed from different paradigms, and their performances vary considerably for different disease models. One important reason for this variability is that their construction is based on a single-correlation model between SNPs and disease. Due to potential model preference and disease complexity, a single-objective method will therefore not work well in general, resulting in low power and a high false-positive rate. In this work, we present a multi-objective heuristic optimization methodology named MACOED for detecting genetic interactions. In MACOED, we combine both logistical regression and Bayesian network methods, which are from opposing schools of statistics. The combination of these two evaluation objectives proved to be complementary, resulting in higher power with a lower false-positive rate than observed for optimizing either objective independently. To solve the space and time complexity for high-dimension problems, a memory-based multi-objective ant colony optimization algorithm is designed in MACOED that is able to retain non-dominated solutions found in past iterations. We compared MACOED with other recent algorithms using both simulated and real datasets. The experimental results demonstrate that our method outperforms others in both detection power and computational feasibility for large datasets. Codes and datasets are available at: www.csbio.sjtu.edu.cn/bioinf/MACOED/.

Evaluation of statistical methods for safety signal detection: a simulation study.

Proactive evaluation of drug safety with systematic screening and detection is critical to protect patients' safety and important in regulatory approval of new drug indications and postmarketing communications and label renewals. In recent years, quite a few statistical methodologies have been developed to better evaluate drug safety through the life cycle of the product development. The statistical methods for flagging safety signals have been developed in two major areas - one for data collected from spontaneous reporting system, mostly in the postmarketing area, and the other for data from clinical trials. To our knowledge, the methods developed for one area have not been applied to the other one so far. In this article, we propose to utilize all such methods for flagging safety signals in both areas regardless of which specific area they were originally developed for. Therefore, we selected eight typical methods, that is, proportional reporting ratios, reporting odds ratios, the maximum likelihood ratio test, Bayesian confidence propagation neural network method, chi-square test for rates comparison, Benjamini and Hochberg procedure, new double false discovery rate control procedure, and Bayesian hierarchical mixture model for systematic comparison through simulations. The Benjamini and Hochberg procedure and new double false discovery rate control procedure perform best overall in terms of sensitivity and false discovery rate. The likelihood ratio test also performs well when the sample sizes are large.

CEO’s commitment bias, ownership concentration, and innovation decision: Behavioral management of CEO’s discretion

AbstractThis paper deals with approving the effect of both a governance system and individual cognitive and emotional features in the financial analysis of a firms’ innovation decision. After discussing the theoretical linking between ownership concentration and the CEO’s attitude and behavior, we are showing on empirical grounds the relationship between the manager’s behavior toward the innovation decision and his cognitive commitment level. The CEO’s commitment bias and attitude conception were measured using a questionnaire. The data analysis was performed using the Bayesian network method on 220 Tunisian managers. In particular, we found that the application of a persuasion mechanism does not have a real impact on the alignment of the manager’s attitude and behavior in key tasks, such as the innovation decision. The CEO’s real behavior was more related to an important individual involvement in this behavior rather than to persuasive effort committed by block holders to make him contract this action. A...

Application of BN in the Fault Diagnosis of Brake Failure System

To solve the problems caused by the complex failure mechanisms in hydraulic brake failure systems and the uncertain linkages between fault type and fault symptoms, a method of Bayesian network (BN) is offered for failure diagnosis. A statistical strategy is adopted there in this algorithm on the rule base provided by many experts to discard the weak causal relationship rules while keep the relatively strong ones during the BN structure learning, and a BN-based model of layered architecture is established for fault diagnosis of hydraulic braking failure systems. Experimental data analysis shows that the Bayesian network fault diagnosis model has higher accuracy than fuzzy logic diagnosis method, effectively solving the uncertainties in fault diagnosis.

Latent structure modeling underlying theophylline tablet formulations using a Bayesian network based on a self-organizing map clustering

The “quality by design” concept in pharmaceutical formulation development requires the establishment of a science-based rationale and design space. In this article, we integrate thin-plate spline (TPS) interpolation, Kohonen’s self-organizing map (SOM) and a Bayesian network (BN) to visualize the latent structure underlying causal factors and pharmaceutical responses. As a model pharmaceutical product, theophylline tablets were prepared using a standard formulation. We measured the tensile strength and disintegration time as response variables and the compressibility, cohesion and dispersibility of the pretableting blend as latent variables. We predicted these variables quantitatively using nonlinear TPS, generated a large amount of data on pretableting blends and tablets and clustered these data into several clusters using a SOM. Our results show that we are able to predict the experimental values of the latent and response variables with a high degree of accuracy and are able to classify the tablet data into several distinct clusters. In addition, to visualize the latent structure between the causal and latent factors and the response variables, we applied a BN method to the SOM clustering results. We found that despite having inserted latent variables between the causal factors and response variables, their relation is equivalent to the results for the SOM clustering, and thus we are able to explain the underlying latent structure. Consequently, this technique provides a better understanding of the relationships between causal factors and pharmaceutical responses in theophylline tablet formulation.

Prediction of Urban Road Congestion Using a Bayesian Network Approach

A reasonable prediction of the possibility of traffic congestion is able to help traffic managers to make efficient decisions to reduce the negative effect of traffic congestion. Previous research has made valuable analyses in this field. However, they rarely consider the dependency and uncertainty of traffic jams. In consideration of the characteristics of traffic congestion from different perspectives, this research proposes a Bayesian Network (BN) analysis approach to predict the possibility of urban traffic congestion. The built-up area of Beijing is taken as the study area of this research. A comprehensive set of variables are utilized to reflect the characteristics of traffic congestion from various viewpoints. The BN method is used to analyze the uncertainty and probability of traffic congestion, and is proved to be fully capable of representing the stochastic nature of traffic congestion. Furthermore, the difference of the congestion probabilities because of applying different urban transport development policies is analyzed in comparison. The study results show that the both road construction and bus system development at the same time can obviously mitigate traffic congestion for the built-up area of Beijing. In the future research, the impact of the comprehensive development of various travel modes on urban traffic congestion needs further studies.