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Relevant bibliographies by topics / Bayesian modelling / Journal articles

Journal articles on the topic 'Bayesian modelling'

To see the other types of publications on this topic, follow the link: Bayesian modelling.

Author: Grafiati

Published: 4 June 2021

Last updated: 11 March 2023

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1

Short, Thomas H. "Applied Bayesian Modelling." Technometrics 46, no. 2 (May 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 (March 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 (March 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 (August 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 (March 19, 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 (February 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 (December 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 (May 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 (February 11, 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 (September 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 (May 30, 2003): 1095–111. http://dx.doi.org/10.1088/0741-3335/45/7/304.

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16

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

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17

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

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18

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

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19

R.M., Harindranath, and Jayanth Jacob. "Bayesian structural equation modelling tutorial for novice management researchers." Management Research Review 41, no. 11 (November 19, 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 Data collected from 172 pharmaceutical sales representatives were used. The study will help the management researchers to perform Bayesian CFA, Bayesian SEM, Bayesian moderation analysis and Bayesian mediation analysis using SPSS AMOS software. Findings The interpretation of the results of Bayesian CFA, Bayesian SEM and Bayesian mediation analysis were discussed. Practical implications The management scholars are non-statisticians and are not much aware of the benefits offered by Bayesian methods. Hitherto, the management scholars use predominantly traditional SEM in validating their models empirically, and this study will give an exposure to “Bayesian statistics” that has practical advantages. Originality/value This is one paper, which discusses the following four concepts: Bayesian method of CFA, SEM, mediation and moderation analysis.

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20

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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21

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 (December 31, 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 Regression. Modelling using Bayesian Geometric Regression show the significant variables are marital status, down payment, installment length, length of stay, and insurance.

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22

Gangopadhyay, A., and W. C. Gau. "Bayesian Nonparametric Approach to Credibility Modelling." Annals of Actuarial Science 2, no. 1 (March 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 same time account for the correlated structure in the data. In this paper, we consider a Bayesian nonparametric approach to the problem of risk modelling. The model incorporates past and present observations related to risk, as well as relevant covariate information. This Bayesian modelling is carried out by sampling from a multivariate Gaussian prior, where the covariance structure is based on a thin-plate spline. The model uses the MCMC technique to compute the predictive distribution of future claims based on available data.

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23

Li, Jackie, and Atsuyuki Kogure. "Bayesian Mixture Modelling for Mortality Projection." Risks 9, no. 4 (April 15, 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 proposed Bayesian approach performs well in fitting and forecasting Japanese mortality.

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24

Abouelela, Mohamed, and Luigi Benedicenti. "Bayesian Network Based XP Process Modelling." International Journal of Software Engineering & Applications 1, no. 3 (July 26, 2010): 1–15. http://dx.doi.org/10.5121/ijsea.2010.1301.

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25

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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26

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 (July 14, 2016): 613–31. http://dx.doi.org/10.1111/rssa.12211.

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27

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 (September 2000): 339–54. http://dx.doi.org/10.1111/1467-9884.00239.

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28

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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29

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

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30

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

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31

Hill, R. "Bayesian decision-making in inventory modelling." IMA Journal of Management Mathematics 10, no. 2 (March 1, 1999): 147–63. http://dx.doi.org/10.1093/imaman/10.2.147.

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32

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 (November 1998): 259–77. http://dx.doi.org/10.1016/s0933-3657(98)00037-2.

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33

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

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34

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

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35

Sykacek, P., R. Clarkson, C. Print, R. Furlong, and G. Micklem. "Bayesian modelling of shared gene function." Bioinformatics 23, no. 15 (May 31, 2007): 1936–44. http://dx.doi.org/10.1093/bioinformatics/btm280.

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36

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

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37

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

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38

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

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39

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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40

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

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41

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

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42

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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43

Amin, Zeinab, and Maram Salem. "Bayesian modelling of health insurance losses." Journal of Applied Statistics 42, no. 2 (August 12, 2014): 231–51. http://dx.doi.org/10.1080/02664763.2014.947247.

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44

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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45

Giudici, Paolo, and Annalisa Bilotta. "Modelling Operational Losses: A Bayesian Approach." Quality and Reliability Engineering International 20, no. 5 (July 29, 2004): 407–17. http://dx.doi.org/10.1002/qre.655.

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46

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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47

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

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48

Young, Martin R. "Robust seasonal adjustment by Bayesian modelling." Journal of Forecasting 15, no. 5 (September 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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49

Freni, Gabriele, Giorgio Mannina, and Gaspare Viviani. "Urban runoff modelling uncertainty: Comparison among Bayesian and pseudo-Bayesian methods." Environmental Modelling & Software 24, no. 9 (September 2009): 1100–1111. http://dx.doi.org/10.1016/j.envsoft.2009.03.003.

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50

Duan, Leo L., Alexander L. Young, Akihiko Nishimura, and David B. Dunson. "Bayesian constraint relaxation." Biometrika 107, no. 1 (December 24, 2019): 191–204. http://dx.doi.org/10.1093/biomet/asz069.

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Summary Prior information often takes the form of parameter constraints. Bayesian methods include such information through prior distributions having constrained support. By using posterior sampling algorithms, one can quantify uncertainty without relying on asymptotic approximations. However, sharply constrained priors are not necessary in some settings and tend to limit modelling scope to a narrow set of distributions that are tractable computationally. We propose to replace the sharp indicator function of the constraint with an exponential kernel, thereby creating a close-to-constrained neighbourhood within the Euclidean space in which the constrained subspace is embedded. This kernel decays with distance from the constrained space at a rate depending on a relaxation hyperparameter. By avoiding the sharp constraint, we enable use of off-the-shelf posterior sampling algorithms, such as Hamiltonian Monte Carlo, facilitating automatic computation in a broad range of models. We study the constrained and relaxed distributions under multiple settings and theoretically quantify their differences. Application of the method is illustrated through several novel modelling examples.

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