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Bayesian Structural Equation Models for Cumulative Theory Building in Information Systems―A Brief Tutorial Using BUGS and R

Joerg Evermann, Mary Tate
2014 Communications of the Association for Information Systems  
Structural equation models (SEM) are frequently used in Information Systems (IS) to analyze and test theoretical propositions.  ...  We advocate the use of Bayesian estimation of structural equation models as an aid to cumulative theory building; Bayesian statistics offer a statistically sound way to incorporate prior knowledge into  ...  Given the extent of structural equation models (SEM) in information systems, this tutorial is specific to the use of Bayesian estimation for SEM.  ... 
doi:10.17705/1cais.03476 fatcat:gx2hqy5ly5gz5js4q5xllx42tu

TREATING UNCERTAINTIES IN A NUCLEAR SEISMIC PROBABILISTIC RISK ASSESSMENT BY MEANS OF THE DEMPSTER-SHAFER THEORY OF EVIDENCE

CHUNG-KUNG LO, N. PEDRONI, E. ZIO
2014 Nuclear Engineering and Technology  
In this paper, a Dempster-Shafer Theory (DST) framework for handling uncertainties in NPP SPRAs is proposed and applied to an example case study.  ...  The main contributions of this paper are two: (i) applying the complete DST framework to SPRA models, showing how to build the Dempster-Shafer structures of the uncertainty parameters based on industry  ...  In section 3, we outline briefly the methodology for uncertainty treatment, demonstrate the building of the Dempster-Shafer structure for some simple general cases and introduce the process of Bayesian  ... 
doi:10.5516/net.03.2014.701 fatcat:pfbvqozoyrcqxnkxbhqjnil6zq

Data-Driven Method for Predicting Remaining Useful Life of Bearing Based on Bayesian Theory

Tianhong Gao, Yuxiong Li, Xianzhen Huang, Changli Wang
2020 Sensors  
Thus, estimating the remaining useful life (RUL) of bearings in real time is of utmost importance. This paper proposes a data-driven RUL prediction method for bearings based on Bayesian theory.  ...  Then, according to Bayesian theory, a Bayesian model of state parameters and bearing life is established.  ...  A Bayesian model in the positive time scale was established using Equation (9) and Equation (12) .  ... 
doi:10.3390/s21010182 pmid:33383918 fatcat:femlv5jvkbdpriskiokkyat72y

Two case studies detailing Bayesian parameter inference for dynamic energy budget models [article]

Philipp H Boersch-Supan, Leah R Johnson
2018 bioRxiv   pre-print
We outline a Bayesian inference approach for energy budget models and provide two case studies -- based on a simplified DEBkiss model, and the standard DEB model -- detailing the implementation of such  ...  Bayesian inference provides a coherent way to estimate parameter uncertainty, and propagate it through the model, while also making use of prior information to constrain the parameter space.  ...  We thank Sadie Ryan, the participants of the 5th International Symposium on Dynamic Energy Budget Theory, and two anonymous reviewers for comments on earlier versions of this work.  ... 
doi:10.1101/259705 fatcat:4elprso45venxbpis5jz3e7qke

Developing a Reliability Model of CNC System under Limited Sample Data Based on Multisource Information Fusion

Chong Peng, Yuzhen Cai, Guangpeng Liu, T. W. Liao
2020 Mathematical Problems in Engineering  
reliability model grounded on Bayesian inference is proposed to deal with the small sample size.  ...  In order to get around the constraint of limited lifetime failure data and take full advantage of existing reliability parameters in traditional reliability models, a multisource information fusion-based  ...  assess the reliability of metering equipment accurately. is paper proposes a new information fusion-based method (as shown in Figure 1 ) for building a reliability model for the CNC system grounded on  ... 
doi:10.1155/2020/3645858 fatcat:m7nnka474vfsvbhdqlcjhh3gri

A Review of Information Fusion Methods for Gas Turbine Diagnostics

Valentina Zaccaria, Moksadur Rahman, Ioanna Aslanidou, Konstantinos Kyprianidis
2019 Sustainability  
Information fusion techniques such as Bayesian networks, fuzzy logic, or probabilistic neural networks can be used to implement a decision support system.  ...  Detecting and isolating faults in a system is a complex problem with many uncertainties, including the integrity of available information.  ...  Equation (8) . ⊗ = 1 1 − ⋂ ∅ ; (7) = ⋂ ∅ . (8) In this case, Equation (7) calculates the cumulative belief for set C given the sources of information A and B.  ... 
doi:10.3390/su11226202 fatcat:kry6gcqnobbtdeuti4kezied7y

Bayesian probabilistic analysis of a nuclear power plant small loss of coolant event tree model with possibilistic parameters [chapter]

Chung-Kung Lo, N Pedroni, E Zio
2013 Safety, Reliability and Risk Analysis  
probabilistic analysis of a nuclear power plant small loss of coolant event tree model with possibilistic parameters", in: R.D.J.M. Steenbergen, P.H.A.  ...  In addition, for comparison purposes Figure 9 also reports the prior and posterior cumulative distributions for the CCDP obtained within a classical, purely probabilistic Bayesian analysis in which the  ...  This work addresses the issue of updating, in a Bayesian framework, the possibilistic representation of the epistemically-uncertain parameters of risk models as new information (e.g., data) becomes available  ... 
doi:10.1201/b15938-505 fatcat:xx4x7eu3hrfxjnq7tssdzxrzvu

Challenge of Multi-Camera Tracking [article]

Yong Wang, Ke Lu
2017 arXiv   pre-print
By analyzing the corresponding characteristics and disadvantages of the existing algorithms, problems in multi-camera tracking are summarized and some new directions for future work are also generalized  ...  Multi-camera tracking is quite different from single camera tracking, and it faces new technology and system architecture challenges.  ...  is the highest, for object tracking 2) Information Fusion with Non Overlapping cameras  Kettnaker and Zabih [38] established an object tracking model for multi-camera system based on Bayesian theory  ... 
arXiv:1702.01507v1 fatcat:t2375mdp5fbtdn3rie55trxgiy

Component-Oriented Reliability Analysis Based on Hierarchical Bayesian Model for an Open Source Software

Yoshinobu Tamura, Hidemitsu Takehara, Shigeru Yamada
2011 American Journal of Operations Research  
The successful experience of adopting distributed development models in such open source projects includes GNU/Linux operating system, Apache HTTP server, Android, BusyBox, and so on.  ...  We propose a method of component-oriented reliability assessment based on hierarchical Bayesian model and Markov chain Monte Carlo methods.  ...  Acknowledgements This work was supported in part by the Grant-in-Aid for Scientific Research (C), Grant No. 22510150 from the Ministry of Education, Culture, Sports, Science, and Technology of Japan.  ... 
doi:10.4236/ajor.2011.12004 fatcat:wnlxgvqppngw3mhapwkqz3rc4i

Modeling Macro-Political Dynamics

Patrick T. Brandt, John R. Freeman
2009 Political Analysis  
We show how a Bayesian structural time series approach addresses them. Our illustration is a structurally identified, nine-equation model of the U.S. political-economic system.  ...  This Bayesian structural model, with a loosely informed prior, yields the best performance in terms of model fit and dynamics.  ...  We construct a nine equation, structurally-identified Bayesian time series model of the U.S. political-economic system.  ... 
doi:10.1093/pan/mpp001 fatcat:3krpqz4cpbch3bsx5kvz5zweje

A Framework for Unifying Formal and Empirical Analysis

Jim Granato, Melody Lo, M. C. Sunny Wong
2010 American Journal of Political Science  
, scientific cumulation.  ...  The first exercise has been described as "data dredging," the second as building "elegant models of irrelevant universes."  ...  No half-measures will suffice if the goal is to build a cumulative science based on the transparency between theory and test.  ... 
doi:10.1111/j.1540-5907.2010.00460.x fatcat:iaibm6tezbdqvntim2jrukynfm

Real-time defect detection of high-speed train wheels by using Bayesian forecasting and dynamic model

Y.W. Wang, Y.Q. Ni, X. Wang
2020 Mechanical systems and signal processing  
., Bayes factor, maximum cumulative Bayes factor and run length) are performed for further identification.  ...  The focus of this study is to develop a real-time defect detection methodology based on Bayesian dynamic linear model (DLM) enabling to detect potentially defective wheels in real time.  ...  Acknowledgements The work described in this paper was supported by a grant from the Research Grants Council of the Hong Kong Special  ... 
doi:10.1016/j.ymssp.2020.106654 fatcat:vxss7waqyjg7dpbck43hqj5cuq

Structural reliability prediction of a steel bridge element using dynamic object oriented Bayesian network (DOOBN)

Ruizi Wang, Lin Ma, Cheng Yan, Joseph Mathew
2011 2011 International Conference on Quality, Reliability, Risk, Maintenance, and Safety Engineering  
, a novel approach based on dynamic objective oriented Bayesian network (DOOBN) for prediction of structural reliability of a steel bridge element has been proposed in this paper.  ...  Moreover, the computational capacity embedded in the approach can be used to facilitate integrated management and maintenance optimization in a bridge system.  ...  ACKNOWLEDGMENT The authors gratefully acknowledge the financial support provided by China Scholar Council (CSC) and Cooperative Research Centre for Infrastructure and Engineering Asset Management (CIEAM  ... 
doi:10.1109/icqr2mse.2011.5976559 fatcat:6pflheql45gw5nhglzuvme5ppu

Fault Diagnosis of DCV and Heating Systems Based on Causal Relation in Fuzzy Bayesian Belief Networks Using Relation Direction Probabilities

Ali Behravan, Bahareh Kiamanesh, Roman Obermaisser
2021 Energies  
The combination of BBN and fuzzy logic in our introduced method analyzes the dependencies of the signals using Mutual Information (MI) theory.  ...  In offline mode, for each fault class, a Relation-Direction Probability (RDP) table is computed and stored in a fault library.  ...  [19] have modeled a Fuzzy Dynamic Bayesian Network (FDBN) for fault diagnosis and reliability prediction in complex systems using various test information.  ... 
doi:10.3390/en14206607 fatcat:q6gtqdzrv5etrkejofkovkwpbq

Inference reasoning on fishers' knowledge using Bayesian causal maps

Louis Bonneau de Beaufort, Karima Sedki, Guy Fontenelle
2015 Ecological Informatics  
Bayesian networks are widely used for decision making processes that face uncertain information or diagnosis. But they are difficult to elicitate.  ...  Cognitive maps and Bayesian networks constitute some useful formalisms to address knowledge representations. Cognitive maps are powerful graphical models for knowledge gathering or displaying.  ...  Acknowledgement This study was funded but the French Liteau program and was led in collaboration with Brest's LETG-Géomer laboratory [Gourmelon et al. 2013 ]. We also are grateful to G.  ... 
doi:10.1016/j.ecoinf.2015.09.006 fatcat:2erleyeg65ft7p4skearzyq73a
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