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Artykuły w czasopismach na temat „Bayesian analysis”

Autor: Grafiati

Data publikacji: 4 czerwca 2021

Data aktualizacji: 9 czerwca 2025

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1

Grossman, J., and M. K. Parmar. "Bayesian analysis." Journal of Epidemiology & Community Health 53, no. 10 (October 1, 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 (December 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 (December 26, 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-management. Research results will enable more accurate forecasting and greater price stability in the hop industry.

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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 (June 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, Hal S. Stern, Donald B. Rubin, Bradley P. Carlin, and Thomas A. Louis. "Bayesian Data Analysis." Journal of the American Statistical Association 92, no. 440 (December 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 (September 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 (September 1996): 1160. http://dx.doi.org/10.2307/2533081.

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9

Katti, S. K. "Robust Bayesian Analysis." Technometrics 43, no. 4 (November 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 (August 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 (December 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 (December 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 (April 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 (April 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 (October 11, 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 (December 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 (January 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 (March 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 (May 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 easier to understand the working behind such algorithms and allowing confidence statements to be made about conclusions. These systems are not necessarily as time consuming as might be expected. However, more care is required in using the knowledge effectively for a given specific problem; this is very much an art rather than a science.

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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 (October 15, 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 (August 2, 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 (October 3, 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 purely Type A, and (2) prior information is available and is represented by a normal distribution. The traditional Bayesian method (also known as the reformulated Bayes Theorem) fails to provide a valid estimate of standard uncertainty in either case. The new modified Bayesian method provides the same solutions to these two cases as its frequentist counterparts. The differences between the new modified Bayesian method and the traditional Bayesian method are discussed. This paper reveals that the traditional Bayesian method is not a self-consistent operation, so it may lead to incorrect inferences in some cases, such as the two cases considered. In the light of the frequentist-Bayesian transformation rule and the law of aggregation of information (LAI), the frequentist and Bayesian inference are virtually equivalent, so they can be unified, at least in measurement uncertainty analysis. The unification is of considerable interest because it may resolve the long-standing debate between frequentists and Bayesians. The unification may also lead to an indisputable, uniform revision of the GUM (Evaluation of measurement data - Guide to the expression of uncertainty in measurement (JCGM 2008)).

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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 (February 28, 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 (October 20, 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 (May 23, 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 illustrates the method with a real example.

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28

Hora, Stephen C., and Ronald L. Iman. "Bayesian Analysis of Learning in Risk Analyses." Technometrics 29, no. 2 (May 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, since domain experts are not necessarily also experts in statistics, we propose Automatic Bayesian Density Analysis (ABDA) to make exploratory data analysis accessible at large. Specifically, ABDA allows for automatic and efficient missing value estimation, statistical data type and likelihood discovery, anomaly detection and dependency structure mining, on top of providing accurate density estimation. Extensive empirical evidence shows that ABDA is a suitable tool for automatic exploratory analysis of mixed continuous and discrete tabular data.

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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 (July 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 (July 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 (January 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 (July 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 (December 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 (March 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 (September 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 (January 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 (August 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 (January 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 the GUSTO trial and other auxiliary data sources. We illustrate Bayesian estimation for proportions, mean costs, and mean quality-of-life weights. The computations are performed using the Bayesian analysis software WinBUGS, distributed by the MRC Biostatistics Unit, Cambridge, England.

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42

Zellner, Arnold. "Bayesian analysis in econometrics." Journal of Econometrics 37, no. 1 (January 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 (July 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 (February 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 (April 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 (July 30, 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 (April 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 (October 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 (June 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 (August 18, 2007): 355–70. http://dx.doi.org/10.1007/s10463-007-0148-y.

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