Bayesian Network Inference Algorithm Research Articles

Study on Fault Diagnosis Method and Application of Automobile Power Supply Based on Fault Tree-Bayesian Network

Among various fault types of automotive power faults, power supply UB + faults have the most complex relationship between fault signs and fault points and are difficult to diagnose. So this paper proposes a Bayesian network fault diagnosis model of automobile power supply based on fault tree. Firstly, based on the in-depth analysis of the principle of automobile power supply fault, the UB + fault tree model is constructed. The fuzzy Bayesian network model of UB + fault is constructed through the mapping relationship between fault tree and Bayesian network. Then, the prior probability of UB + fault points are obtained according to the five-year fault dataset of FAW Volkswagen Reck system, and the relevant conditional probabilities are determined by fuzzy set theory due to the lack of data and the uncertainty in expert scoring. Finally, the relevant fault point probability values are determined according to the Bayesian network inference algorithm in the case of single or parallel UB + fault sign occurring, and the fault diagnosis sequence is guided, further improving fault diagnosis efficiency.

Bayesian network variable elimination method optimal elimination order construction

Variable Elimination (VE) is the most basic one of many Bayesian network inference algorithms. The speed and complexity of reasoning mainly depend on the order of elimination. Finding the optimal elimination order is a Nondeterministic Polynomial Hard (NP-Hard) problem, which is often solved by heuristic search in practice. In order to improve the speed of reasoning of the variable elimination method, the minimum, maximum potential, minimum missing edge and minimum added complexity search methods are studied. The Asian network is taken as an example to analyze and calculate the complexity and elimination of the above search method. Meta-order, through MATLAB R2018a, the above different search methods were constructed and reasoned separately. Finally, the performance of the four search methods was compared by inference time analysis. The experimental results show that the minimum increase complexity search method is better than other search methods, and the average time consuming is at least 0.012s, which can speed up the reasoning process of Bayesian network.

A matrix analytic approach for Bayesian network modeling and inference of a manufacturing system

A shared digital twin of a manufacturing system is valuable to provide maintenance services in a collaborative manner. An accurate analytical model of the Bayesian network is crucial to depict endogenous failure mechanisms in the digital twin. Nodes’ connections in Bayesian networks correspond to a range of linear, bilinear or multilinear mappings over finite-state variables. The conditional probability table (CPT) of a child node can be represented as a k-dimensional tensor if it has (k-1) parent nodes. A new matrix analytic approach is proposed for Bayesian network inference based on the theory of semi-tensor product. The matrix representation of probabilistic networks is firstly studied, and a specific parameter training algorithm is constructed based on the matrix model. Bayesian network inference algorithms, including both forward and backward reasoning, are then presented for reliability analysis and fault diagnosis. A real manufacturing system is applied to verify the proposed approach. This matrix analytic approach helps to study the Bayesian network’s mathematical properties, and it is proved to be convenient and efficient in probability network modeling and inference.

Fish ecotyping based on machine learning and inferred network analysis of chemical and physical properties

Functional diversity rather than species richness is critical for the understanding of ecological patterns and processes. This study aimed to develop novel integrated analytical strategies for the functional characterization of fish diversity based on the quantification, prediction and integration of the chemical and physical features in fish muscles. Machine learning models with an improved random forest algorithm applied on 1867 muscle nuclear magnetic resonance spectra belonging to 249 fish species successfully predicted the mobility patterns of fishes into four categories (migratory, territorial, rockfish, and demersal) with accuracies of 90.3–95.4%. Markov blanket-based feature selection method with an ecological–chemical–physical integrated network based on the Bayesian network inference algorithm highlighted the importance of nitrogen metabolism, which is critical for environmental adaptability of fishes in nutrient-rich environments, in the functional characterization of fish biodiversity. Our study provides valuable information and analytical strategies for fish home-range assessment on the basis of the chemical and physical characterization of fish muscle, which can serve as an ecological indicator for fish ecotyping and human impact monitoring.

First network analysis of interspecific associations of abyssal benthic megafauna reveals potential vulnerability of abyssal hill community

The distribution of organisms is related to both environmental factors and interactions between organisms. However, such associations between organisms across an abyssal megafaunal community have not previously been investigated at landscape scale because of a lack of positional data on specimens over such scales. We quantified spatial distributions and investigated interspecific associations in benthic megafaunal communities in three contrasting habitats on the Porcupine Abyssal Plain, two on the abyssal plain and one on the flank of a modest abyssal hill (~50 m above the plain). We used a Bayesian Network Inference Algorithm approach, which considers the ecosystem as a network, facilitated by robust positioning of specimens determined through seabed photography captured with an autonomous underwater vehicle. We found non-random intraspecific distributions of most morphotypes in all areas. The organisms in two interspecific networks on the abyssal plain had high connectance and link density, while the network at the Hill site was notable in the lack of inter-dependencies and highly dependent on one morphotype – Ophiuroidea. The reduced connectance of the hill community suggests that it is operating under a different regime and potentially more vulnerable to perturbation than those on the plain. Interspecific dependencies on the abyssal plain occurred across broad taxonomic groupings, and were thought to be a result of similar relationships between pairs of organisms and the substrate, and competition for detrital resource. In addition, some intraspecific pairs changed dependency direction at different scales. Our results suggest that the scales of inter- and intraspecific aggregation will be important considerations in the design of community assessments, and in spatial planning for their conservation.

Tuxnet: a simple interface to process RNA sequencing data and infer gene regulatory networks.

Predicting gene regulatory networks (GRNs) from expression profiles is a common approach for identifying important biological regulators. Despite the increased use of inference methods, existing computational approaches often do not integrate RNA-sequencing data analysis, are not automated or are restricted to users with bioinformatics backgrounds. To address these limitations, we developed tuxnet, a user-friendly platform that can process raw RNA-sequencing data from any organism with an existing reference genome using a modified tuxedo pipeline (hisat2+cufflinks package) and infer GRNs from these processed data. tuxnet is implemented as a graphical user interface and can mine gene regulations, either by applying a dynamic Bayesian network(DBN) inference algorithm, genist, or a regression tree-based pipeline, rtp-star. We obtained time-course expression data of a PERIANTHIA (PAN) inducible line and inferred a GRN using genist to illustrate the use of tuxnet while gaining insight into the regulations downstream of the Arabidopsis root stem cell regulator PAN. Using rtp-star, we inferred the network of ATHB13, a downstream gene of PAN, for which we obtained wild-type and mutant expression profiles. Additionally, we generated two networks using temporal data from developmental leaf data and spatial data from root cell-type data to highlight the use of tuxnet to form new testable hypotheses from previously explored data. Our case studies feature the versatility of tuxnet when using different types of gene expression data to infer networks and its accessibility as a pipeline for non-bioinformaticians to analyze transcriptome data, predict causal regulations, assess network topology and identify key regulators.

Advanced hybrid stochastic dynamic Bayesian network inference algorithm development in the context of the web applications test execution

The article is devoted to the application of dynamic Bayesian networks models for fuzzing web applications and development of effective hybrid algorithms for probabilistic inference based on particle filter algorithm. Dynamic Bayesian networks models allow to simulate the dynamic process transformation of web applications associated with the process of their constant instrumental and logical updates, and create a probabilistic structure required for learning process of testing the top web applications vulnerabilities, that able to use the evidence and inference results obtained in the retrospective and current testing slices and improve testing mechanisms in new time slices. The hybrid probabilistic inference algorithm for dynamic Bayesian networks models for testing web-applications, proposed in the current research, significantly increase the efficiency of the classical approximate probabilistic inference algorithms, well reflect the features of the temporary testing links formation and adapted to the detection of anomalous errors.

A Review of Inference Algorithms for Hybrid Bayesian Networks

Hybrid Bayesian networks have received an increasing attention during the last years. The difference with respect to standard Bayesian networks is that they can host discrete and continuous variables simultaneously, which extends the applicability of the Bayesian network framework in general. However, this extra feature also comes at a cost: inference in these types of models is computationally more challenging and the underlying models and updating procedures may not even support closed-form solutions. In this paper we provide an overview of the main trends and principled approaches for performing inference in hybrid Bayesian networks. The methods covered in the paper are organized and discussed according to their methodological basis. We consider how the methods have been extended and adapted to also include (hybrid) dynamic Bayesian networks, and we end with an overview of established software systems supporting inference in these types of models.

On Darwinian Networks

We suggest Darwinian Networks (DNs) as a simplification of working with Bayesian networks (BNs). DNs adapt a handful of well‐known concepts in biology into a single framework that is surprisingly simple yet remarkably robust. With respect to modeling, on one hand, DNs not only represent BNs but also faithfully represent the testing of independencies in a more straightforward fashion. On the other hand, with respect to three exact inference algorithms in BNs, DNs simplify each of them while unifying all of them. DNs can determine good elimination orderings using the same platform as used for modeling and inference. Finally, we demonstrate how DNs can represent two additional frameworks. Practical benefits of DNs include faster algorithms for inference and modeling.

Bayesian-network-based system identification of spatial distribution of structural parameters

Structural deterioration or damage in civil infrastructures may results in severe losses of properties and human lives. To prevent such situations, structure health monitoring (SHM) technologies have been developed in recent decades. As one of the SHM technologies, so-called system identification (SI) methods aim to estimate structural parameters by minimizing an error function consisting of measured and calculated responses of a structure under the same loading condition. Such optimization-based SI algorithms may suffer from ill-posedness of the inverse problem, which may result in non-uniqueness of solutions or non-stability of the optimization process. In this paper, in order to avoid issues related to ill-posedness, an SI method based on the Bayesian Network (BN) technology is developed, especially for probabilistic identification of spatial distribution of structural parameters. Utilizing graph theories, a BN describes random variables by nodes connected by links, which represent conditional probability tables (CPT) explaining the probabilistic relationship between the linked nodes. For effective SI using BN, this study proposes a BN graph model, which employs a bi-variate Gaussian function as a shape function describing the spatial distribution of a structural parameter with a small number of parameters. The parameters of the Gaussian (shape) function are considered as nodes in the BN to describe various spatial distribution patterns using a small number of parent nodes. Using this modeling approach, the number of the BN nodes is not affected by the size of the finite element (FE) mesh. Thus, the approach prevents the BN graph from growing to an intractable size even if the FE analysis requires smaller meshes to improve the accuracy of the structural analysis. Using the constructed BN, information on applied loads, and observed structural responses, a BN inference algorithm effectively updates the prior distribution of spatial distribution parameters to the posterior distribution. The proposed method is tested and demonstrated by numerical examples. Using a variety of structural deterioration scenarios, the SI results by the proposed method are compared with those by maximum likelihood estimation (MLE), and an FE-updating method. During the test, the influences of measurement errors, and incomplete/missing data on the performance of the methods are also investigated. The results show that the proposed SI approach is more stable and robust than the other tested methods, and potentially has more merits that are worth investigating in future research.

Interspecific networks in ground beetle (Coleoptera: Carabidae) assemblages

Although changes to interspecific relationships can significantly alter the composition of insect assemblages, they are often ignored when assessing impacts of environmental change. Long-term ground beetle data were used in this study to analyse ecological networks from three habitats at two sites in Scotland. A Bayesian Network inference algorithm was used to reveal interspecific relationships. The significance and strength of relationships between species (nodes) were estimated along with other network properties. Links were identified as positive relationships if co-occurrences of beetles correlated positively, and as negatives relationships if there was a negative correlation between the occurrences of the species. Most of the species had few links and only 10% of the nodes were connected with several links. Calathus fuscipes, a common carabid in the samples, was the most connected, with nine links to other species. More interspecific relationships were found to be positive than negative, with 48 and 23 links, respectively. The modular structure of the network was assessed and eight separate sub-networks were found. Habitat preferences of the species were clearly represented in the structure of the sets of those five sub-networks containing more than one species and were in line with the findings of the indicator species analysis. In our study, we showed that generated Bayesian networks can model interspecific relationships between carabid species. Due to the relative ease of the collection of field data and the high information content of the results, this method could be incorporated into everyday ecological analysis.

Simulation Studies Informed by RNA‐seq Data Suggest the Utility of a Multi‐network Bayesian Graphical Model Algorithm for the Study of Hypertension in the Dahl S Rat

In the past, we used static Bayesian graphical models to investigate the role of individual genes in the development of hypertension. Here, we present a simulation study for an experimental design that will allow us to directly compare the networks of gene-gene interactions in the Dahl S (SS) rat and in SS rats with GWAS genes associated with hypertension knocked out (KO). RNA-seq was performed on 8 tissues from an SS rat and two KO rats, which had been fed either a 0.4% or 4% salt diet for 7 days. The list of transcripts whose expression values met a fold change cutoff in the SS but not the KO rats underwent ontology analysis using DAVID, which found pathways that were overrepresented in the list and the number of differentially regulated genes in those pathways; this number was used to modify networks from our previous graphical network study. For each pathway, three networks were created: one which mirrored the previous pathway and two in which five or ten connections were changed. Data was then simulated from these networks and analyzed using a Bayesian network inference algorithm. The algorithm was sufficiently powered by our simulated data set to accurately describe differences between simulated pathways and to elucidate the structure of those pathways, implying that our experimental design has the potential to yield valuable insights into the role of GWAS genes in the onset of hypertension. (HL082798)

Evaluation of a two-stage framework for prediction using big genomic data.

We are in the era of abundant 'big' or 'high-dimensional' data. These data afford us the opportunity to discover predictors of an event of interest, and to estimate occurrence of the event based on values of these predictors. For example, 'genome-wide association studies' examine millions of single-nucleotide polymorphisms (SNPs), along with disease status. We can learn SNPs that affect disease status from these data sets, and use the knowledge learned to predict disease likelihood. Owing to the large number of features, it is difficult for many prediction methods to use all the features directly. The ReliefF algorithm ranks a set of features in terms of how well they predict a target. It can be used to identify good predictors, which can then be provided to a prediction method. We compared the performance of eight prediction methods when predicting binary outcomes using high-dimensional discrete data sets. We performed two-stage prediction, where ReliefF is used in the first stage to identify good predictors. Bayesian network (BN)-based methods performed best overall. Furthermore, ReliefF did not improve their performance. The BN-based methods use the Bayesian Dirichlet Equivalent Uniform score to evaluate candidate models, and use BN inference algorithms to perform prediction. This score and these algorithms were developed for discrete variables. This perhaps explains why they perform better in this domain. Many prediction methods are available, and researchers have little reason for choosing one over the other in the domain of binary prediction using high-dimensional data sets. Our results indicate that the best choices overall are BN-based methods.

The Intelligent Fault Diagnosis Based on Bayesian Network Inference Algorithm Solution

In this thesis a remote intelligent fault diagnosis system based on wireless network is put forward. A method of data-transmission based on wireless network is analyzed and a scheme for intelligent fault diagnosis on the basis of Bayesian network is laid out.This thesis also researches on how to build a fault diagnosis model taking advantage of Bayesian network and how to improve the system's resolving ability and reasoning quality in the process of intelligent fault diagnosis.

Computational complexity of local posteriori inference algorithms in algebraic Bayesian networks.

The paper presents algorithmical complexity estimates for local posteriori inference in algebraic Bayesian networks. We consider the ways of implementing the inference for three types of evidence (deterministic, stochastic, and imprecise). If we need to solve linear programming tasks for inference, the comlexity estimations are given in numbers of such tasks and numbers of variables and constraints in each task. In other cases, complexity estimates are given in numbers of arithmetic operations.

A Bayesian Assessment Method of Network Risk

Based on system analysis of information security risk factors and evaluation process, aiming at the uncertainty information is difficult to quantify the evaluation process, Bayesian network inference algorithm, and combined with the inference rules of conditional probability matrix is given by expert knowledge of Bayesian network, the evaluation model of information security risk. Finally an instance of the risk assessment approach on the model is analyzed which demonstrates the rationality and feasibility of this method. So it provides a new method for information security assessment.

System Reliability Evaluation Using Bayesian Networks

Bayesian network method for system reliability evaluation which is based on a Bayesian network that transformed from a fault tree has gotten much attention these years. After a brief introduction to the method how to transform a fault tree into a Bayesian network, the paper elaborates the Bayesian network inference algorithms. The paper focuses on the way how the inference algorithms can be applied to the practice of system reliability evaluation and designs a systematic flow chart used to evaluate system reliability in a Bayesian network way. The experiment demonstrates the feasibility of the systematic flow chart.

Programming algorithm for reference of discrete dynamic Bayesian network

The discrete dynamic Bayesian network is a useful tool for modeling and inferring the time series progress,and it has wide modeling application value.But its inference algorithm needs improving.Concerning the shortcomings of the discrete dynamic Bayesian network,such as its inference algorithm is hard to understand,hard to program and running slowly,this paper proposed the most suitable storage data structure of the discrete dynamic Bayesian network,deduced the fast inference algorithm for the discrete dynamic Bayesian network,and verified the inference algorithm for the discrete dynamic Bayesian networks through a sample.

A novel Bayesian network inference algorithm for integrative analysis of heterogeneous deep sequencing data

A novel Bayesian network inference algorithm for integrative analysis of heterogeneous deep sequencing data

Abstract IA9: The MEK network as a target for combination therapies

Abstract Small molecule inhibitors of signal transduction pathways can elicit complex and sometimes non-intuitive molecular responses in cancer cells. Increasing evidence supports the notion that these responses are important mediators of de novo drug resistance. We systematically assessed molecular and cellular responses to MEK inhibitors, aiming at identifying meaningful targets for combination therapies. Proteomic analyses revealed an EGFR-dependent feedback loop resulting in activation of the PI3 kinase pathway following MEK inhibition. In agreement with this, combinations of MEK inhibitors and inhibitors of EGFR inhibitors or PI3 kinase show synergistic effects on cell proliferation and apoptosis rates in molecularly defined disease subtypes. To further explore molecular networks affected by MEK inhibition, Bayesian network inference algorithms were applied and reverse-engineered networks were generated that are amenable to in silico forward-simulation. Using this technology, new, MEK-dependent, novel regulators of cell cycle and apoptosis were identified and experimentally validated. These findings highlight the potential of mathematical modeling approaches to efficiently reveal new and therapeutically relevant features of signal transduction networks.