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Journal articles on the topic 'Bayesian analysis'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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1

Grossman, J., and M. K. Parmar. "Bayesian analysis." Journal of Epidemiology & Community Health 53, no. 10 (1999): 652b—653b. http://dx.doi.org/10.1136/jech.53.10.652b.

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2

Grunkemeier, Gary L., and Nicola Payne. "Bayesian analysis." Annals of Thoracic Surgery 74, no. 6 (2002): 1901–8. http://dx.doi.org/10.1016/s0003-4975(02)04535-6.

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3

MacKinnon, Douglas, and Martin Pavlovič. "A Bayesian analysis of hop price fluctuations." Agricultural Economics (Zemědělská ekonomika) 66, No. 12 (2020): 519–26. http://dx.doi.org/10.17221/239/2020-agricecon.

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This paper quantifies the correlation between U.S. season average prices for hops with U.S. hop stocks and U.S. hop hectarage. The Hop Equilibrium Ratio, a measure of the supply/demand relationship for U.S. hops, was introduced. Through the Bayesian inference method, the authors used these data to calculate the effect an incremental change to one metric had on the probability of directional changes of future U.S. season average prices (SAP). Between 2010 and 2020, the dominance of proprietary varieties created unprecedented cartel-like powers offering opportunities for supply- and price-manage

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4

Forbes, Florence, and Adrian E. Raftery. "Bayesian Morphology: Fast Unsupervised Bayesian Image Analysis." Journal of the American Statistical Association 94, no. 446 (1999): 555–68. http://dx.doi.org/10.1080/01621459.1999.10474150.

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5

Brooks, Stephen, A. Gelman, J. B. Carlin, H. S. Stern, and D. B. Rubin. "Bayesian Data Analysis." Statistician 45, no. 2 (1996): 266. http://dx.doi.org/10.2307/2988417.

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6

Halloran, M. Elizabeth, Andrew Gelman, John B. Carlin, et al. "Bayesian Data Analysis." Journal of the American Statistical Association 92, no. 440 (1997): 1640. http://dx.doi.org/10.2307/2965436.

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7

Dixon, Dennis O., and Richard Simon. "Bayesian Subset Analysis." Biometrics 47, no. 3 (1991): 871. http://dx.doi.org/10.2307/2532645.

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8

Fearn, T., A. Gelman, J. B. Carlin, H. S. Stern, and D. B. Rubin. "Bayesian Data Analysis." Biometrics 52, no. 3 (1996): 1160. http://dx.doi.org/10.2307/2533081.

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9

Katti, S. K. "Robust Bayesian Analysis." Technometrics 43, no. 4 (2001): 493. http://dx.doi.org/10.1198/tech.2001.s53.

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10

Grego, John. "Bayesian Data Analysis." Technometrics 46, no. 3 (2004): 363–64. http://dx.doi.org/10.1198/tech.2004.s199.

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11

Yan, Jun. "Bayesian Survival Analysis." Journal of the American Statistical Association 99, no. 468 (2004): 1202–3. http://dx.doi.org/10.1198/jasa.2004.s359.

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12

Yuan, Ying, and David P. MacKinnon. "Bayesian mediation analysis." Psychological Methods 14, no. 4 (2009): 301–22. http://dx.doi.org/10.1037/a0016972.

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13

May, Margaret. "Bayesian Survival Analysis." International Journal of Epidemiology 31, no. 2 (2002): 505. http://dx.doi.org/10.1093/intjepid/31.2.505.

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14

May, Margaret. "Bayesian Survival Analysis." International Journal of Epidemiology 31, no. 2 (2002): 505. http://dx.doi.org/10.1093/ije/31.2.505.

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15

Yang, Shengping, and Gilbert Berdine. "Bayesian data analysis." Southwest Respiratory and Critical Care Chronicles 8, no. 36 (2020): 74–77. http://dx.doi.org/10.12746/swrccc.v8i36.773.

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16

Avramov, Doron, and Guofu Zhou. "Bayesian Portfolio Analysis." Annual Review of Financial Economics 2, no. 1 (2010): 25–47. http://dx.doi.org/10.1146/annurev-financial-120209-133947.

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17

Neath, Andrew A. "Bayesian Data Analysis." Journal of Quality Technology 31, no. 1 (1999): 127. http://dx.doi.org/10.1080/00224065.1999.11979904.

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18

Hill, Bruce M., Harry F. Martz, and Ray A. Waller. "Bayesian Reliability Analysis." Journal of the American Statistical Association 80, no. 389 (1985): 253. http://dx.doi.org/10.2307/2288105.

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19

Carpenter, Jacque, Byron Gajewski, Cynthia Teel, and Lauren S. Aaronson. "Bayesian Data Analysis." Nursing Research 57, no. 3 (2008): 214–19. http://dx.doi.org/10.1097/01.nnr.0000319495.59746.b8.

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20

Mardia, K. V. "Bayesian Image Analysis." Journal of Theoretical Medicine 1, no. 1 (1997): 63–77. http://dx.doi.org/10.1080/10273669708833007.

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Bayes' theorem is a vehicle for incorporating prior knowledge in updating the degree of belief in light of data. For example, the state of tomorrow's weather can be predicted using belief or likelihood of tomorrow's weather given today's weather data. We give a brief review of the recent advances in the area with emphasis on high-level Bayesian image analysis. It has been gradually recognised that knowledge-based algorithms based on Bayesian analysis are more widely applicable and reliable than ad hoc algorithms. Advantages include the use of explicit and realistic statistic models making it e

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21

Black, T. C., and W. J. Thompson. "Bayesian data analysis." Computing in Science & Engineering 3, no. 4 (2001): 86–91. http://dx.doi.org/10.1109/5992.931908.

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22

BEST, NICKY. "BAYESIAN DATA ANALYSIS." Statistics in Medicine 15, no. 19 (1996): 2123–24. http://dx.doi.org/10.1002/(sici)1097-0258(19961015)15:19<2123::aid-sim364>3.0.co;2-k.

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23

Kruschke, John K. "Bayesian data analysis." Wiley Interdisciplinary Reviews: Cognitive Science 1, no. 5 (2010): 658–76. http://dx.doi.org/10.1002/wcs.72.

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24

Huang, Hening. "A new modified Bayesian method for measurement uncertainty analysis and the unification of frequentist and Bayesian inference." Journal of Probability and Statistical Science 20, no. 1 (2022): 52–79. http://dx.doi.org/10.37119/jpss2022.v20i1.515.

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This paper proposes a new modification of the traditional Bayesian method for measurement uncertainty analysis. The new modified Bayesian method is derived from the law of aggregation of information (LAI) and the rule of transformation between the frequentist view and Bayesian view. It can also be derived from the original Bayes Theorem in continuous form. We focus on a problem that is often encountered in measurement science: a measurement gives a series of observations. We consider two cases: (1) there is no genuine prior information about the measurand, so the uncertainty evaluation is pure

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25

Pinacho-Ríos, Araceli, and Guillermo De la Torre-Gea. "Analysis of Maternal Deaths in Oaxaca through Bayesian Networks." International Journal of Trend in Scientific Research and Development Volume-2, Issue-2 (2018): 1658–63. http://dx.doi.org/10.31142/ijtsrd10758.

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26

Min, Seung-Ki, Andreas Hense, Heiko Paeth, and Won Tae Kwon. "A Bayesian decision method for climate change signal analysis." Meteorologische Zeitschrift 13, no. 5 (2004): 421–36. http://dx.doi.org/10.1127/0941-2948/2004/0013-0421.

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27

Hicks, Tyler, Liliana Rodríguez-Campos, and Jeong Hoon Choi. "Bayesian Posterior Odds Ratios." American Journal of Evaluation 39, no. 2 (2017): 278–89. http://dx.doi.org/10.1177/1098214017704302.

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To begin statistical analysis, Bayesians quantify their confidence in modeling hypotheses with priors. A prior describes the probability of a certain modeling hypothesis apart from the data. Bayesians should be able to defend their choice of prior to a skeptical audience. Collaboration between evaluators and stakeholders could make their choices more defensible. This article describes how evaluators and stakeholders could combine their expertise to select rigorous priors for analysis. The article first introduces Bayesian testing, then situates it within a collaborative framework, and finally

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28

Hora, Stephen C., and Ronald L. Iman. "Bayesian Analysis of Learning in Risk Analyses." Technometrics 29, no. 2 (1987): 221–28. http://dx.doi.org/10.1080/00401706.1987.10488213.

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29

Cui, Kai, and Wenshan Cui. "Bayesian Factorized Cointegration Analysis." Open Journal of Statistics 02, no. 05 (2012): 504–11. http://dx.doi.org/10.4236/ojs.2012.25065.

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30

Kosugi, Koji, Kazuhiro Maeda, and Makoto Hirakawa. "Doing Bayesian Data Analysis." Proceedings of the Annual Convention of the Japanese Psychological Association 81 (September 20, 2017): TWS—011—TWS—011. http://dx.doi.org/10.4992/pacjpa.81.0_tws-011.

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31

Vergari, Antonio, Alejandro Molina, Robert Peharz, Zoubin Ghahramani, Kristian Kersting, and Isabel Valera. "Automatic Bayesian Density Analysis." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 5207–15. http://dx.doi.org/10.1609/aaai.v33i01.33015207.

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Making sense of a dataset in an automatic and unsupervised fashion is a challenging problem in statistics and AI. Classical approaches for exploratory data analysis are usually not flexible enough to deal with the uncertainty inherent to real-world data: they are often restricted to fixed latent interaction models and homogeneous likelihoods; they are sensitive to missing, corrupt and anomalous data; moreover, their expressiveness generally comes at the price of intractable inference. As a result, supervision from statisticians is usually needed to find the right model for the data. However, s

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32

Wang, Dong, and Xiaoyang Tan. "Bayesian Neighborhood Component Analysis." IEEE Transactions on Neural Networks and Learning Systems 29, no. 7 (2018): 3140–51. http://dx.doi.org/10.1109/tnnls.2017.2712823.

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33

Forbes, Catherine S., Guyonne R. J. Kalb, and Paul Kofman. "Bayesian Arbitrage Threshold Analysis." Journal of Business & Economic Statistics 17, no. 3 (1999): 364. http://dx.doi.org/10.2307/1392294.

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34

Cook, Peyton. "Bayesian autoregressive spectral analysis." Communications in Statistics - Theory and Methods 14, no. 5 (1985): 1001–18. http://dx.doi.org/10.1080/03610928508828959.

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35

Forbes, Catherine S., Guyonne R. J. Kalb, and Paul Kofhian. "Bayesian Arbitrage Threshold Analysis." Journal of Business & Economic Statistics 17, no. 3 (1999): 364–72. http://dx.doi.org/10.1080/07350015.1999.10524825.

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36

Huang, Jing, and Ying Yuan. "Bayesian dynamic mediation analysis." Psychological Methods 22, no. 4 (2017): 667–86. http://dx.doi.org/10.1037/met0000073.

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37

Fearnhead, Paul, and Despina Vasileiou. "Bayesian Analysis of Isochores." Journal of the American Statistical Association 104, no. 485 (2009): 132–41. http://dx.doi.org/10.1198/jasa.2009.0009.

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38

Berliner, L. Mark, and Bruce M. Hill. "Bayesian Nonparametric Survival Analysis." Journal of the American Statistical Association 83, no. 403 (1988): 772–79. http://dx.doi.org/10.1080/01621459.1988.10478660.

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39

Besag, Julian. "Towards Bayesian image analysis." Journal of Applied Statistics 20, no. 5-6 (1993): 107–19. http://dx.doi.org/10.1080/02664769300000061.

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40

WESTERN, BRUCE. "Bayesian Analysis for Sociologists." Sociological Methods & Research 28, no. 1 (1999): 7–34. http://dx.doi.org/10.1177/0049124199028001002.

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41

Fryback, Dennis G., James O. Chinnis, and Jacob W. Ulvila. "BAYESIAN COST-EFFECTIVENESS ANALYSIS." International Journal of Technology Assessment in Health Care 17, no. 1 (2001): 83–97. http://dx.doi.org/10.1017/s0266462301104083.

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A desirable element of cost-effectiveness analysis (CEA) modeling is a systematic way to relate uncertainty about input parameters to uncertainty in the computational results of the CEA model. Use of Bayesian statistical estimation and Monte Carlo simulation provides a natural way to compute a posterior probability distribution for each CEA result. We demonstrate this approach by reanalyzing a previously published CEA evaluating the incremental cost-effectiveness of tissue plasminogen activator compared to streptokinase for thrombolysis in acute myocardial infarction patients using data from t

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42

Zellner, Arnold. "Bayesian analysis in econometrics." Journal of Econometrics 37, no. 1 (1988): 27–50. http://dx.doi.org/10.1016/0304-4076(88)90072-3.

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43

Steel, Mark F. J., and Jean-Francois Richard. "Bayesian multivariate exogeneity analysis." Journal of Econometrics 49, no. 1-2 (1991): 239–74. http://dx.doi.org/10.1016/0304-4076(91)90015-6.

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44

Quintana, Fernando A., and Peter M�ller. "Nonparametric Bayesian Data Analysis." Statistical Science 19, no. 1 (2004): 95–110. http://dx.doi.org/10.1214/088342304000000017.

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45

Cowan, Glen. "Data analysis: Frequently Bayesian." Physics Today 60, no. 4 (2007): 82–83. http://dx.doi.org/10.1063/1.2731991.

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46

Hilbe, Joseph M. "Bayesian Analysis with Stata." International Statistical Review 83, no. 2 (2015): 331–32. http://dx.doi.org/10.1111/insr.12111_6.

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47

Perez Ruiz, Diego Andres. "Bayesian Nonparametric Data Analysis." International Statistical Review 84, no. 1 (2016): 157–58. http://dx.doi.org/10.1111/insr.12168.

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48

Hayashi, Kentaro, and Ke-Hai Yuan. "Robust Bayesian Factor Analysis." Structural Equation Modeling: A Multidisciplinary Journal 10, no. 4 (2003): 525–33. http://dx.doi.org/10.1207/s15328007sem1004_2.

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49

Mosleh, Ali, E. Richard Hilton, and Peter S. Browne. "Bayesian probabilistic risk analysis." ACM SIGMETRICS Performance Evaluation Review 13, no. 1 (1985): 5–12. http://dx.doi.org/10.1145/1041838.1041839.

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50

Alvarez, Enrique E., and Dipak K. Dey. "Bayesian isotonic changepoint analysis." Annals of the Institute of Statistical Mathematics 61, no. 2 (2007): 355–70. http://dx.doi.org/10.1007/s10463-007-0148-y.

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