Matrix-based Bayesian Network for efficient memory storage and flexible inference

Byun, Ji-Eun; Zwirglmaier, Kilian; Straub, Daniel; Song, Junho

doi:10.1016/j.ress.2019.01.007

Cited by 18 publications

(10 citation statements)

References 25 publications

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“…extended the matrix formulation of the MSR method to solve k-out-of-N system problems in their efforts to evaluate the reliability growth of a complex system. The matrixbased formulation of the events and their corresponding probabilities in the LP bounds (Song and Der Kiureghian 2003) and MSR (Kang et al 2008;Song and Kang 2009) methods led to the development of a matrix-based Bayesian network (MBN) (Byun et al 2019), which utilizes an alternative matrix-based data structure of the probability mass functions for efficient memory storage and flexible inference. Kang and Song (2010) developed the sequential compounding method (SCM) to approximate rapidly the reliability of general systems.…”

Section: Methods For General Systemsmentioning

confidence: 99%

Structural System Reliability: Overview of Theories and Applications to Optimization

Song

Kang

Lee

et al. 2021

ASCE-ASME J. Risk Uncertainty Eng. Syst., Part A: Civ. Eng.

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This paper provides an overview of theories and applications of structural system reliability (SSR). The paper defines SSR problems and discusses the growing needs for SSR analysis and technical challenges. Detailed literature reviews are provided for three subtopics: SSR methods for Boolean system events, SSR methods for sequential failures, and SSR-based design/topology optimization. Discussions of each subtopic define the target problem using mathematical formulations and categorize existing SSR methods in terms of the characteristics of the problems and approaches. The paper summarizes SSR methods that are considered critical in the history and have introduced notable technological developments in recent years. In each subtopic or category, the reviewed methods are compared with each other in terms of accuracy, computational efficiency, and implementation issues to allow identifying apposite methods for SSR applications. The paper concludes with remarks on future research needs and opportunities.

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Section: Methods For General Systemsmentioning

confidence: 99%

Structural System Reliability: Overview of Theories and Applications to Optimization

Song

Kang

Lee

et al. 2021

ASCE-ASME J. Risk Uncertainty Eng. Syst., Part A: Civ. Eng.

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“…This means that the conditional distributions of EDPs and IMs do not need to be defined for those inappropriate model parameter values. To address this issue, this paper uses the matrix-based Bayesian network (MBN) proposed in Byun et al 45 The MBN utilizes an alternative matrix-based formulation of conditional probabilities for efficient memory storage and flexible inference. Therefore, the MBN provides an ideal means of overcoming the aforementioned issue of inexhaustive distribution.…”

Section: F I G U R Ementioning

confidence: 99%

“…Therefore, the MBN provides an ideal means of overcoming the aforementioned issue of inexhaustive distribution. More details and other advantages of the MBN can be found in Byun et al 45 and Byun and Song. 46 This paper uses the data structure and inference algorithms of the MBN to evaluate the seismic fragility under various conditions of main and aftershocks using the BN model structure in Figure 9.…”

Section: F I G U R Ementioning

confidence: 99%

Bayesian‐network‐based risk modeling and inference for structures under a sequence of main and aftershocks

Mun

Song

2022

Earthq Engng Struct Dyn

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A sequence of main and aftershocks is a complex natural hazard, which may introduce severe damage to critical infrastructure systems. Managing the risk caused by sequential earthquake events is challenging due to the uncertainties of aftershocks and their dependency on the preceding mainshock. Moreover, the dynamic behavior of a structural system under aftershocks must be evaluated with consideration of possible structural damage caused by the mainshock. To overcome these difficulties, this paper proposes a new framework based on a Bayesian network (BN) for risk modelling and inference for structures under a sequence of main and aftershocks. The stochastic mechanisms of sequential earthquakes and the hysteretic behavior of structures are described by BN models, which are then integrated for comprehensive modeling of the risk. The developed BN model facilitates probabilistic inference to obtain fragility curves given various circumstances regarding sequential earthquake events and observations. Numerical examples demonstrate fragility assessment and inference with the proposed BN-based framework.

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“…The Bayesian network model is used to create a more complex process, and it needs experts' participation to determine the network nodes to solve problems related to field knowledge through repeated research and constantly improving the Bayesian network structure. Byun et al [38] propose a matrix-based Bayesian network (MBN) that facilitates efficient modeling based on joint probability, mass functions and flexible inference. In addition, the Bayesian network analysis also appears in supply chain management [39], risk management [40], project decision-making [41]and environmental simulation [42].…”

Section: Bayesian Network and Reliability Distributionmentioning

confidence: 99%

Online Car-Hailing System Performance Analysis Based on Bayesian Network

2019

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The concept of the sharing economy has attracted wide attention due to its huge impact on transforming traditional industries. Online car-hailing, combining the sharing economy and ICT technologies, shapes a new landscape, which can greatly shorten the traveler's waiting time and reduce the emptyrun rate of cars (such as Uber and DiDi). However, a few comprehensive studies have been conducted on the sustainable development of online car-hailing considering both the user experience and the operational cost. To address this issue, this paper systematically studies the influencing factors, their relations, and their impacts on online car-hailing in an empirical way. First, an index system in four key aspects, namely service, price, safety, and traveling time, is established to evaluate the user experience. Second, the Bayesian network theory is employed to model the complexity of each factor and the extent of its influence on the online car-hailing system with expert scores. The two most important influence paths affecting the passengers' choices of online car-hailing are determined. Third, we further construct an investment allocation model with the aim of minimizing the economic cost of the online car-hailing system while maintaining the system performance, considering the limiting factors, such as the complexity and cost. Finally, we perform a simulation experiment, which generates some practical suggestions for improving the online car-hailing system.

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Matrix-based Bayesian Network for efficient memory storage and flexible inference

Cited by 18 publications

References 25 publications

Structural System Reliability: Overview of Theories and Applications to Optimization

Structural System Reliability: Overview of Theories and Applications to Optimization

Bayesian‐network‐based risk modeling and inference for structures under a sequence of main and aftershocks

Online Car-Hailing System Performance Analysis Based on Bayesian Network

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