Simulation metamodelling with Bayesian networks

Pousi, Jouni; Poropudas, Jirka; Virtanen, Kai

doi:10.1057/jos.2013.18

Cited by 11 publications

(3 citation statements)

References 27 publications

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“…Moreover, using the identified discretization intervals for variables X 1 1 , X 1 2 , X1 1 , X1 2 , function MD maps these variables to the relevant discretised input vactor of step 2 (u = 1, 2): MD (X 1 u ) = RC u , MD ( X1 u ) = RV u . We can now estimate the conditional probabilities by using maximum likelihood estimators (see page 299 Pousi et al, 2013) as follows:…”

Section: Step 3: Building Bayesian Network Modelmentioning

confidence: 99%

Simulation Supported Bayesian Network Approach for Performance Assessment of Infrastructure Systems

Songhori¹,

Fecarotti²,

Houtum³

2022

Book of Extended Abstracts for the 32nd European Safety and Reliability Conference

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Section: Step 3: Building Bayesian Network Modelmentioning

confidence: 99%

Simulation Supported Bayesian Network Approach for Performance Assessment of Infrastructure Systems

Songhori¹,

Fecarotti²,

Houtum³

2022

Book of Extended Abstracts for the 32nd European Safety and Reliability Conference

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“…This paper, therefore, deploys a meta-modeling approach rather than modeling the real system such that the inputs and the outputs of the system are used to learn its behavior. This approach has been effectively applied in several engineering domains (Fienen et al, 2016;Pousi et al, 2013). Dikmen et al (2020) adopted a similar meta-modeling approach to capture associations IJPPM 71,1 among project complexity, uncertainty and performance.…”

Section: Literature Reviewmentioning

confidence: 99%

Data-driven impact assessment of multidimensional project complexity on project performance

Qazi

2020

IJPPM

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PurposeThe purpose of this paper is to propose a data-driven scheme for identifying critical project complexity dimensions and establishing the trade-off across multiple project performance criteria.Design/methodology/approachThis paper adopts a hybrid approach using Bayesian Belief Networks (BBNs) and Artificial Neural Networks (ANNs). The output of the ANN model is used as input to the BBN model for prioritizing project complexity dimensions relative to multiple project performance criteria. The proposed process is demonstrated through a real application in the construction industry.FindingsWith a number of nonlinear interactions involved within and across project complexity and performance, it is not feasible to model and assess the strength of these interactions using conventional techniques. The proposed process helps in effectively mapping a “multidimensional complexity” space to a “multidimensional performance” space and makes use of data from past projects for operationalizing this mapping scheme by means of ANNs. This obviates the need for developing a parametric model that is both challenging and computationally cumbersome. The mapping function can be used for generating all possible scenarios required for the development of a data-driven BBN model.Originality/valueThis paper introduces a data-driven process for operationalizing the mapping of project complexity to project performance within a network setting of interacting complexity dimensions and performance criteria. The results of the application study manifest the importance of capturing the interdependency across project complexity and performance. Ignoring the underlying interdependencies and relying exclusively on conventional correlation-based techniques may lead to making suboptimal decisions.

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“…For the input variables, the relative frequencies in the simulation data do not necessarily reflect the actual probability distributions in question because they can be modified as part of the design of experiment step in order to collect a broader set of data. The probability distributions of the input variables are adjusted after the construction of the DBN metamodel in order to represent input certainty (Pousi et al, 2013). The distributions can be modified only after validating the metamodel, because the adjusted distributions for the inputs are not consistent with the validation data.…”

Section: Construction Of Dbn Metamodelsmentioning

confidence: 99%

Simulation Metamodeling using Dynamic Bayesian Networks with Multiple Time Scales

Harju¹,

Virtanen²,

Poropudas³

2018

Linköping Electronic Conference Proceedings

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The utilization of dynamic Bayesian networks (DBNs) in simulation metamodeling enables the investigation of the time evolution of state variables of a simulation model. DBN metamodels have previously described the changes in the probability distribution of the simulation state by using a time slice structure in which the state variables are described at common time instants. In this paper, the novel approach to the determination of the time slice structure is introduced. It enables the selection of time instants of the DBN separately for each state variable. In this way, a more accurate metamodel representing multiple time scales of the variables is achieved. Furthermore, the construction is streamlined by presenting a dynamic programming algorithm for determining the key time instants for individual variables. The construction and use of the DBN metamodels are illustrated by an example problem dealing with the simulated operation of an air base.

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Simulation metamodelling with Bayesian networks

Cited by 11 publications

References 27 publications

Simulation Supported Bayesian Network Approach for Performance Assessment of Infrastructure Systems

Simulation Supported Bayesian Network Approach for Performance Assessment of Infrastructure Systems

Data-driven impact assessment of multidimensional project complexity on project performance

Simulation Metamodeling using Dynamic Bayesian Networks with Multiple Time Scales

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