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Journal articles on the topic 'Bayesian modelling'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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1

Short, Thomas H. "Applied Bayesian Modelling." Technometrics 46, no. 2 (2004): 249–50. http://dx.doi.org/10.1198/004017004000000293.

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2

Cowles, Mary Kathryn. "Bayesian Statistical Modelling." Journal of the American Statistical Association 98, no. 461 (2003): 256–57. http://dx.doi.org/10.1198/jasa.2003.s262.

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3

Müller, Peter. "Applied Bayesian Modelling." Journal of the American Statistical Association 100, no. 469 (2005): 355–56. http://dx.doi.org/10.1198/jasa.2005.s12.

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4

Ganocy, Stephen J. "Bayesian Statistical Modelling." Technometrics 44, no. 3 (2002): 291–92. http://dx.doi.org/10.1198/004017002320256495.

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5

Congdon, Peter. "Bayesian Statistical Modelling." Measurement Science and Technology 13, no. 4 (2002): 643. http://dx.doi.org/10.1088/0957-0233/13/4/703.

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6

Gunn, Roger, V. Schmid, B. Whitcher, and V. Cunningham. "Bayesian kinetic modelling." NeuroImage 31 (January 2006): T71. http://dx.doi.org/10.1016/j.neuroimage.2006.04.061.

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7

Denison, D. G. T., N. M. Adams, C. C. Holmes, and D. J. Hand. "Bayesian partition modelling." Computational Statistics & Data Analysis 38, no. 4 (2002): 475–85. http://dx.doi.org/10.1016/s0167-9473(01)00073-1.

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8

Svensén, Markus, and Christopher M. Bishop. "Robust Bayesian mixture modelling." Neurocomputing 64 (March 2005): 235–52. http://dx.doi.org/10.1016/j.neucom.2004.11.018.

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9

Skene, A. M., J. E. H. Shaw, and T. D. Lee. "Bayesian Modelling and Sensitivity Analysis." Statistician 35, no. 2 (1986): 281. http://dx.doi.org/10.2307/2987533.

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10

Pettit, Lawrence. "Book Review: Bayesian statistical modelling." Statistical Methods in Medical Research 11, no. 6 (2002): 554. http://dx.doi.org/10.1177/096228020201100608.

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11

Zeidman, Peter, Edward Harry Silson, Dietrich Samuel Schwarzkopf, Chris Ian Baker, and Will Penny. "Bayesian population receptive field modelling." NeuroImage 180 (October 2018): 173–87. http://dx.doi.org/10.1016/j.neuroimage.2017.09.008.

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12

Currie, C. S. M. "Bayesian methodology for dynamic modelling." Journal of Simulation 1, no. 2 (2007): 97–107. http://dx.doi.org/10.1057/palgrave.jos.4250014.

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13

Dua, Pami. "Macroeconomic Modelling and Bayesian Methods." Journal of Quantitative Economics 15, no. 2 (2017): 209–26. http://dx.doi.org/10.1007/s40953-017-0077-4.

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14

Bergman, Bo, and Min Xie. "On Bayesian software reliability modelling." Journal of Statistical Planning and Inference 29, no. 1-2 (1991): 33–41. http://dx.doi.org/10.1016/0378-3758(92)90119-d.

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15

Fischer, R., A. Dinklage, and E. Pasch. "Bayesian modelling of fusion diagnostics." Plasma Physics and Controlled Fusion 45, no. 7 (2003): 1095–111. http://dx.doi.org/10.1088/0741-3335/45/7/304.

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16

Aguilera, P. A., A. Fernández, R. Fernández, R. Rumí, and A. Salmerón. "Bayesian networks in environmental modelling." Environmental Modelling & Software 26, no. 12 (2011): 1376–88. http://dx.doi.org/10.1016/j.envsoft.2011.06.004.

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17

Clarke, Bertrand. "Information optimality and Bayesian modelling." Journal of Econometrics 138, no. 2 (2007): 405–29. http://dx.doi.org/10.1016/j.jeconom.2006.05.003.

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18

Klugman, Stuart A. "Bayesian modelling of mortality catastrophes." Insurance: Mathematics and Economics 8, no. 3 (1989): 159–64. http://dx.doi.org/10.1016/0167-6687(89)90053-x.

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19

Ayuputri, Ikacipta Mega, Nur Iriawan, and Pratnya Paramitha Oktaviana. "Frequency Model of Credit Payment using Bayesian Geometric Regression and Bayesian Mixture Geometric Regression." MATEMATIKA 34, no. 3 (2018): 103–13. http://dx.doi.org/10.11113/matematika.v34.n3.1143.

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In distributing funds to customers as credit, multi-finance companies have two necessary risks, i.e. prepayment risk, and default risk. The default risk can be minimized by determining the factors that affect the survival of customers to make credit payment, in terms of frequency of credit payments by customers that are distributed geometry. The proposed modelling is using Bayesian Geometric Regression and Bayesian Mixture Geometric Regression. The best model of this research is modelling using Bayesian Geometric Regression method because it has lower DIC values than Bayesian Mixture Geometric

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20

R.M., Harindranath, and Jayanth Jacob. "Bayesian structural equation modelling tutorial for novice management researchers." Management Research Review 41, no. 11 (2018): 1254–70. http://dx.doi.org/10.1108/mrr-11-2017-0377.

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Purpose This paper aims to popularize the Bayesian methods among novice management researchers. The paper interprets the results of Bayesian method of confirmatory factor analysis (CFA), structural equation modelling (SEM), mediation and moderation analysis, with the intention that the novice researchers will apply this method in their research. The paper made an attempt in discussing various complex mathematical concepts such as Markov Chain Monte Carlo, Bayes factor, Bayesian information criterion and deviance information criterion (DIC), etc. in a lucid manner. Design/methodology/approach D

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21

Barons, Martine J., Anca M. Hanea, Steven Mascaro, and Owen Woodberry. "Reporting Standards for Bayesian Network Modelling." Entropy 27, no. 1 (2025): 69. https://doi.org/10.3390/e27010069.

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Reproducibility is a key measure of the veracity of a modelling result or finding. In other research areas, notably in medicine, reproducibility is supported by mandating the inclusion of an agreed set of details into every research publication, facilitating systematic reviews, transparency and reproducibility. Governments and international organisations are increasingly turning to modelling approaches in the development and decision-making for policy and have begun asking questions about accountability in model-based decision making. The ethical issues of relying on modelling that is biased,

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22

Marcot, Bruce G., and Trent D. Penman. "Advances in Bayesian network modelling: Integration of modelling technologies." Environmental Modelling & Software 111 (January 2019): 386–93. http://dx.doi.org/10.1016/j.envsoft.2018.09.016.

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23

Ghahramani, Zoubin. "Bayesian non-parametrics and the probabilistic approach to modelling." Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 371, no. 1984 (2013): 20110553. http://dx.doi.org/10.1098/rsta.2011.0553.

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Modelling is fundamental to many fields of science and engineering. A model can be thought of as a representation of possible data one could predict from a system. The probabilistic approach to modelling uses probability theory to express all aspects of uncertainty in the model. The probabilistic approach is synonymous with Bayesian modelling, which simply uses the rules of probability theory in order to make predictions, compare alternative models, and learn model parameters and structure from data. This simple and elegant framework is most powerful when coupled with flexible probabilistic mo

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24

Gangopadhyay, A., and W. C. Gau. "Bayesian Nonparametric Approach to Credibility Modelling." Annals of Actuarial Science 2, no. 1 (2007): 91–114. http://dx.doi.org/10.1017/s1748499500000270.

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ABSTRACTCurrent methods in credibility theory often rely on parametric models, e.g., a linear function of past experience. During the last decade, the existence of high speed computers and statistical software packages allowed the introduction of more sophisticated and flexible modelling strategies. In recent years, some of these techniques, which made use of the Markov Chain Monte Carlo (MCMC) approach to modelling, have been incorporated in credibility theory. However, very few of these methods made use of additional covariate information related to risk, or collection of risks; and at the s

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25

Li, Jackie, and Atsuyuki Kogure. "Bayesian Mixture Modelling for Mortality Projection." Risks 9, no. 4 (2021): 76. http://dx.doi.org/10.3390/risks9040076.

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Although a large number of mortality projection models have been proposed in the literature, relatively little attention has been paid to a formal assessment of the effect of model uncertainty. In this paper, we construct a Bayesian framework for embedding more than one mortality projection model and utilise the finite mixture model concept to allow for the blending of model structures. Under this framework, the varying features of different model structures can be exploited jointly and coherently to have a more detailed description of the underlying mortality patterns. We show that the propos

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26

Abouelela, Mohamed, and Luigi Benedicenti. "Bayesian Network Based XP Process Modelling." International Journal of Software Engineering & Applications 1, no. 3 (2010): 1–15. http://dx.doi.org/10.5121/ijsea.2010.1301.

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27

Agrawal, Swati. "Software Quality Modelling Using Bayesian Networks." IOSR Journal of Computer Engineering 8, no. 5 (2013): 52–62. http://dx.doi.org/10.9790/0661-0855262.

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28

Vallejos, Catalina A., and Mark F. J. Steel. "Bayesian survival modelling of university outcomes." Journal of the Royal Statistical Society: Series A (Statistics in Society) 180, no. 2 (2016): 613–31. http://dx.doi.org/10.1111/rssa.12211.

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29

Fan, Y., and S. P. Brooks. "Bayesian Modelling of Prehistoric Corbelled Domes." Journal of the Royal Statistical Society: Series D (The Statistician) 49, no. 3 (2000): 339–54. http://dx.doi.org/10.1111/1467-9884.00239.

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30

Anderson, Craig, Duncan Lee, and Nema Dean. "Bayesian cluster detection via adjacency modelling." Spatial and Spatio-temporal Epidemiology 16 (February 2016): 11–20. http://dx.doi.org/10.1016/j.sste.2015.11.005.

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31

P., Weber, and Jouffe L. "Reliability modelling with dynamic bayesian networks." IFAC Proceedings Volumes 36, no. 5 (2003): 57–62. http://dx.doi.org/10.1016/s1474-6670(17)36470-4.

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32

KOTTAS, ATHANASIOS, and MILOVAN KRNJAJIĆ. "Bayesian Semiparametric Modelling in Quantile Regression." Scandinavian Journal of Statistics 36, no. 2 (2009): 297–319. http://dx.doi.org/10.1111/j.1467-9469.2008.00626.x.

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33

Hill, R. "Bayesian decision-making in inventory modelling." IMA Journal of Management Mathematics 10, no. 2 (1999): 147–63. http://dx.doi.org/10.1093/imaman/10.2.147.

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34

Chevrolat, Jean-Paul, Jean-Louis Golmard, Salomon Ammar, Roland Jouvent, and Jean-François Boisvieux. "Modelling behavioral syndromes using Bayesian networks." Artificial Intelligence in Medicine 14, no. 3 (1998): 259–77. http://dx.doi.org/10.1016/s0933-3657(98)00037-2.

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35

de Zoete, Jacob, Marjan Sjerps, David Lagnado, and Norman Fenton. "Modelling crime linkage with Bayesian networks." Science & Justice 55, no. 3 (2015): 209–17. http://dx.doi.org/10.1016/j.scijus.2014.11.005.

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36

Fung, Wing-Kam, and J. Bacon-Shone. "Quasi-Bayesian modelling of multivariate outliers." Computational Statistics & Data Analysis 16, no. 3 (1993): 271–78. http://dx.doi.org/10.1016/0167-9473(93)90129-h.

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37

Sykacek, P., R. Clarkson, C. Print, R. Furlong, and G. Micklem. "Bayesian modelling of shared gene function." Bioinformatics 23, no. 15 (2007): 1936–44. http://dx.doi.org/10.1093/bioinformatics/btm280.

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38

Tjelmeland, Håkon, and Kjetill Vassmo Lund. "Bayesian modelling of spatial compositional data." Journal of Applied Statistics 30, no. 1 (2003): 87–100. http://dx.doi.org/10.1080/0266476022000018547.

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39

Puustelli, Anne, Lasse Koskinen, and Arto Luoma. "Bayesian modelling of financial guarantee insurance." Insurance: Mathematics and Economics 43, no. 2 (2008): 245–54. http://dx.doi.org/10.1016/j.insmatheco.2008.07.001.

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40

McCluskey, Andrew, and Tim Snow. "uravu: Making Bayesian modelling easy(er)." Journal of Open Source Software 5, no. 50 (2020): 2214. http://dx.doi.org/10.21105/joss.02214.

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41

Jr, Eugene Santos, Keum Joo Kim, Fei Yu, Deqing Li, and Joseph Rosen. "Bayesian knowledge modelling for healthcare practices." International Journal of Simulation and Process Modelling 8, no. 1 (2013): 52. http://dx.doi.org/10.1504/ijspm.2013.055207.

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42

Duncan, Kristin A., and Steven N. MacEachern. "Nonparametric Bayesian modelling for item response." Statistical Modelling: An International Journal 8, no. 1 (2008): 41–66. http://dx.doi.org/10.1177/1471082x0700800104.

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43

Karabatsos, George, and Stephen G. Walker. "Coherent psychometric modelling with Bayesian nonparametrics." British Journal of Mathematical and Statistical Psychology 62, no. 1 (2009): 1–20. http://dx.doi.org/10.1348/000711007x246237.

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44

Albert, Jim. "Introduction to Bayesian item response modelling." International Journal of Quantitative Research in Education 2, no. 3/4 (2015): 178. http://dx.doi.org/10.1504/ijqre.2015.071732.

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45

Amin, Zeinab, and Maram Salem. "Bayesian modelling of health insurance losses." Journal of Applied Statistics 42, no. 2 (2014): 231–51. http://dx.doi.org/10.1080/02664763.2014.947247.

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46

Anderson, Jon E. "Random effect modelling using Bayesian methods." International Journal of Services Technology and Management 8, no. 4/5 (2007): 316. http://dx.doi.org/10.1504/ijstm.2007.013922.

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47

Chen, Serena H., and Carmel A. Pollino. "Good practice in Bayesian network modelling." Environmental Modelling & Software 37 (November 2012): 134–45. http://dx.doi.org/10.1016/j.envsoft.2012.03.012.

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48

Lucas, Peter J. F. "Bayesian network modelling through qualitative patterns." Artificial Intelligence 163, no. 2 (2005): 233–63. http://dx.doi.org/10.1016/j.artint.2004.10.011.

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49

Young, Martin R. "Robust seasonal adjustment by Bayesian modelling." Journal of Forecasting 15, no. 5 (1996): 355–67. http://dx.doi.org/10.1002/(sici)1099-131x(199609)15:5<355::aid-for625>3.0.co;2-k.

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50

Giudici, Paolo, and Annalisa Bilotta. "Modelling Operational Losses: A Bayesian Approach." Quality and Reliability Engineering International 20, no. 5 (2004): 407–17. http://dx.doi.org/10.1002/qre.655.

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