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Статті в журналах з теми "Bayesian interpretation"

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Автор: Grafiati

Опубліковано: 10 грудня 2022

Оновлено: 28 січня 2023

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1

Listokin, Yair. "Bayesian Contractual Interpretation." Journal of Legal Studies 39, no. 2 (June 2010): 359–74. http://dx.doi.org/10.1086/652459.

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2

Giovannelli, J. F., and J. Idier. "Bayesian interpretation of periodograms." IEEE Transactions on Signal Processing 49, no. 7 (July 2001): 1388–96. http://dx.doi.org/10.1109/78.928692.

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3

Kumar, V. P., and U. B. Desai. "Image interpretation using Bayesian networks." IEEE Transactions on Pattern Analysis and Machine Intelligence 18, no. 1 (1996): 74–77. http://dx.doi.org/10.1109/34.476423.

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4

Jones, W. Paul. "Bayesian Interpretation of Test Reliability." Educational and Psychological Measurement 51, no. 3 (September 1991): 627–35. http://dx.doi.org/10.1177/0013164491513009.

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5

THORNDIKE, ROBERT L. "Bayesian Concepts and Test Interpretation." Journal of Counseling & Development 65, no. 3 (November 1986): 170–71. http://dx.doi.org/10.1002/j.1556-6676.1986.tb01269.x.

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6

Cho, Hyun Ja, Eun Young Kwack, and Chul Soon Choi. "Bayesian approach in interpretation of mammography." Journal of the Korean Radiological Society 27, no. 6 (1991): 901. http://dx.doi.org/10.3348/jkrs.1991.27.6.901.

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7

Kim, Sang Joon. "Bayesian interpretation of eyewitness statement data." KOREAN JOURNAL OF FORENSIC PSYCHOLOGY 8, no. 2 (July 31, 2017): 61–110. http://dx.doi.org/10.53302/kjfp.2017.07.8.2.61.

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8

Elble, Rodger J. "Bayesian Interpretation of Essential Tremor Plus." Journal of Clinical Neurology 18, no. 2 (2022): 127. http://dx.doi.org/10.3988/jcn.2022.18.2.127.

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9

Wan, E. A. "Neural network classification: a Bayesian interpretation." IEEE Transactions on Neural Networks 1, no. 4 (1990): 303–5. http://dx.doi.org/10.1109/72.80269.

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10

Laganière, Robert, and Amar Mitiche. "Direct Bayesian interpretation of visual motion." Robotics and Autonomous Systems 14, no. 4 (June 1995): 247–54. http://dx.doi.org/10.1016/0921-8890(94)00018-w.

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11

McNevin, Dennis. "Bayesian interpretation of discrete class characteristics." Forensic Science International 292 (November 2018): 125–30. http://dx.doi.org/10.1016/j.forsciint.2018.09.013.

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12

Mei, Wei. "Bridging Probability and Possibility via Bayesian Theorem." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 22, no. 04 (August 2014): 615–26. http://dx.doi.org/10.1142/s0218488514500317.

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Анотація:

The connection between probability and possibility has been studied for decades. Several studies have intended to provide a unified interpretation for these concepts; other studies attempt to discover their transformation relationship. This paper addresses these problems using a unified perspective. By extending the viewpoint of interpreting the grade of membership as a conditional probability, we introduce the conditional probability mass function and likelihood mass function to describe randomness and fuzziness, respectively. We draw the conclusion that conditional probability is undetermined itself and can be used for describing either randomness or fuzziness, depending on how it is interpreted. A fuzzy Bayesian theorem is presented based on the fuzziness interpretation of conditional probability. Additionally, a probability-possibility conversion is derived by assigning different interpretations for conditional probability of the Bayesian theorem, which is notably similar to Klir's normalized transformation. An example of target recognition demonstrates that the fuzzy Bayesian classifier outperforms the usual Bayesian classifier.

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13

Spalburg, Mirano R. "A Fast and Transparent Bayesian Log Interpretation Method." Petrophysics – The SPWLA Journal of Formation Evaluation and Reservoir Description 63, no. 4 (August 1, 2022): 534–48. http://dx.doi.org/10.30632/pjv63n4-2022a4.

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Анотація:

This paper presents a fast and transparent Bayesian-based computational method that can be used for the interpretation of well logs. Rather than single values for formation properties, the results are probability distributions. The method is fast because the responses of logging tools, for a large set of formation realizations, are calculated only once and then stored with the realizations in a database. Thereafter, only this database is used for the interpretation of real well logs. The method is transparent because it entirely relies on selecting formation realizations with calculated log responses that are, within a given error margin, equal to the real logging tool responses. Therefore, only internally consistent interpretations can be found. The size of the database is realizable and the log evaluation computation time sufficiently short. The method has been tested with good results on more than 100 wells. Some of the well results are presented and discussed. The presented evaluations use databases of about 300 MB containing about 25 million realizations. Database construction and whole well evaluation each require less than a few seconds.

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14

Greenland, Sander. "Bayesian Interpretation and Analysis of Research Results." Seminars in Hematology 45, no. 3 (July 2008): 141–49. http://dx.doi.org/10.1053/j.seminhematol.2008.04.004.

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15

Teixeira, Filipe, and M. Natália D. S. Cordeiro. "Improving Vibrational Mode Interpretation Using Bayesian Regression." Journal of Chemical Theory and Computation 15, no. 1 (December 10, 2018): 456–70. http://dx.doi.org/10.1021/acs.jctc.8b00439.

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16

Sacha, Jarosław P., Lucy S. Goodenday, and Krzysztof J. Cios. "Bayesian learning for cardiac SPECT image interpretation." Artificial Intelligence in Medicine 26, no. 1-2 (September 2002): 109–43. http://dx.doi.org/10.1016/s0933-3657(02)00055-6.

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17

Kolm, Petter, and Gordon Ritter. "On the Bayesian interpretation of Black–Litterman." European Journal of Operational Research 258, no. 2 (April 2017): 564–72. http://dx.doi.org/10.1016/j.ejor.2016.10.027.

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18

Wasserman, Larry Alan. "A Robust Bayesian Interpretation of Likelihood Regions." Annals of Statistics 17, no. 3 (September 1989): 1387–93. http://dx.doi.org/10.1214/aos/1176347277.

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19

El-Gamal, Mahmoud A. "A BAYESIAN INTERPRETATION OF MULTIPLE POINT ESTIMATES." Econometric Reviews 20, no. 2 (April 30, 2001): 235–45. http://dx.doi.org/10.1081/etc-100103825.

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20

Taylor, Greg. "A Bayesian interpretation of Whittaker—Henderson graduation." Insurance: Mathematics and Economics 11, no. 1 (April 1992): 7–16. http://dx.doi.org/10.1016/0167-6687(92)90084-o.

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21

Caelen, Olivier. "A Bayesian interpretation of the confusion matrix." Annals of Mathematics and Artificial Intelligence 81, no. 3-4 (September 11, 2017): 429–50. http://dx.doi.org/10.1007/s10472-017-9564-8.

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22

Lin, Weikang, and Mustapha Ishak. "A Bayesian interpretation of inconsistency measures in cosmology." Journal of Cosmology and Astroparticle Physics 2021, no. 05 (May 1, 2021): 009. http://dx.doi.org/10.1088/1475-7516/2021/05/009.

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23

Yoon, Ji Won. "Bayesian interpretation to generalize adaptive mean shift algorithm." Journal of Intelligent & Fuzzy Systems 30, no. 6 (April 30, 2016): 3583–92. http://dx.doi.org/10.3233/ifs-162103.

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24

Tyers, Cathy. "Bayesian Interpretation of Tree-Ring Dates in Practice." Vernacular Architecture 39, no. 1 (December 2008): 91–106. http://dx.doi.org/10.1179/174962908x365082.

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25

Srinivasan, Preethi, M. Brandon Westover, and Matt T. Bianchi. "Propagation of Uncertainty in Bayesian Diagnostic Test Interpretation." Southern Medical Journal 105, no. 9 (September 2012): 452–59. http://dx.doi.org/10.1097/smj.0b013e3182621a2c.

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26

Miller, G., H. Martz, T. Little, and L. Bertelli. "BAYESIAN HYPOTHESIS TESTING—USE IN INTERPRETATION OF MEASUREMENTS." Health Physics 94, no. 3 (March 2008): 248–54. http://dx.doi.org/10.1097/01.hp.0000290624.35701.00.

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27

Davis, Sergio, Joaquín Peralta, Yasmín Navarrete, Diego González, and Gonzalo Gutiérrez. "A Bayesian Interpretation of First-Order Phase Transitions." Foundations of Physics 46, no. 3 (November 4, 2015): 350–59. http://dx.doi.org/10.1007/s10701-015-9967-5.

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28

Lassiter, Daniel, and Noah D. Goodman. "Adjectival vagueness in a Bayesian model of interpretation." Synthese 194, no. 10 (June 23, 2015): 3801–36. http://dx.doi.org/10.1007/s11229-015-0786-1.

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29

Schaapveld, Tayla E. M., Stephanie L. Opperman, and SallyAnn Harbison. "Bayesian networks for the interpretation of biological evidence." Wiley Interdisciplinary Reviews: Forensic Science 1, no. 3 (January 28, 2019): e1325. http://dx.doi.org/10.1002/wfs2.1325.

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30

MacKay, David J. C. "Bayesian Interpolation." Neural Computation 4, no. 3 (May 1992): 415–47. http://dx.doi.org/10.1162/neco.1992.4.3.415.

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Анотація:

Although Bayesian analysis has been in use since Laplace, the Bayesian method of model-comparison has only recently been developed in depth. In this paper, the Bayesian approach to regularization and model-comparison is demonstrated by studying the inference problem of interpolating noisy data. The concepts and methods described are quite general and can be applied to many other data modeling problems. Regularizing constants are set by examining their posterior probability distribution. Alternative regularizers (priors) and alternative basis sets are objectively compared by evaluating the evidence for them. “Occam's razor” is automatically embodied by this process. The way in which Bayes infers the values of regularizing constants and noise levels has an elegant interpretation in terms of the effective number of parameters determined by the data set. This framework is due to Gull and Skilling.

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31

Castañon, Eduardo, Álvaro Sanchez-Arraez, Paula Jimenez-Fonseca, Felipe Alvarez-Manceñido, Irene Martínez-Martínez, Luka Mihic Gongora, and Alberto Carmona-Bayonas. "Bayesian interpretation of immunotherapy trials with dynamic treatment effects." European Journal of Cancer 161 (January 2022): 79–89. http://dx.doi.org/10.1016/j.ejca.2021.11.002.

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32

Qiu, Cheng, and Alan Stocker. "Bayesian interpretation of artificial neural network models in perception." Journal of Vision 21, no. 9 (September 27, 2021): 2712. http://dx.doi.org/10.1167/jov.21.9.2712.

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33

Brase, Gary L. "Frequency interpretation of ambiguous statistical information facilitates Bayesian reasoning." Psychonomic Bulletin & Review 15, no. 2 (April 2008): 284–89. http://dx.doi.org/10.3758/pbr.15.2.284.

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34

Bernard, Jean-Marc. "Bayesian Interpretation of Frequentist Procedures for a Bernoulli Process." American Statistician 50, no. 1 (February 1996): 7. http://dx.doi.org/10.2307/2685036.

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35

Held, Leonhard. "On the Bayesian interpretation of the harmonic meanp-value." Proceedings of the National Academy of Sciences 116, no. 13 (March 19, 2019): 5855–56. http://dx.doi.org/10.1073/pnas.1900671116.

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36

Bernard, Jean-Marc. "Bayesian Interpretation of Frequentist Procedures for a Bernoulli Process." American Statistician 50, no. 1 (February 1996): 7–13. http://dx.doi.org/10.1080/00031305.1996.10473534.

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37

Hancock, Edwin R., and Marcello Pelillo. "A Bayesian interpretation for the exponential correlation associative memory." Pattern Recognition Letters 19, no. 2 (February 1998): 149–59. http://dx.doi.org/10.1016/s0167-8655(97)00164-5.

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38

Palge, Veiko, and Thomas Konrad. "A remark on Fuchs’ Bayesian interpretation of quantum mechanics." Studies in History and Philosophy of Science Part B: Studies in History and Philosophy of Modern Physics 39, no. 2 (May 2008): 273–87. http://dx.doi.org/10.1016/j.shpsb.2007.10.002.

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39

Liseo, Brunero, and Nicola Loperfido. "A Bayesian interpretation of the multivariate skew-normal distribution." Statistics & Probability Letters 61, no. 4 (February 2003): 395–401. http://dx.doi.org/10.1016/s0167-7152(02)00398-x.

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40

Adankon, M. M., M. Cheriet, and A. Biem. "Semisupervised Learning Using Bayesian Interpretation: Application to LS-SVM." IEEE Transactions on Neural Networks 22, no. 4 (April 2011): 513–24. http://dx.doi.org/10.1109/tnn.2011.2105888.

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41

de Baar, Jouke H. S., Richard P. Dwight, and Hester Bijl. "IMPROVEMENTS TO GRADIENT-ENHANCED KRIGING USING A BAYESIAN INTERPRETATION." International Journal for Uncertainty Quantification 4, no. 3 (2014): 205–23. http://dx.doi.org/10.1615/int.j.uncertaintyquantification.2013006809.

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42

LITTON, C. D., and C. E. BUCK. "THE BAYESIAN APPROACH TO THE INTERPRETATION OF ARCHAEOLOGICAL DATA." Archaeometry 37, no. 1 (February 1995): 1–24. http://dx.doi.org/10.1111/j.1475-4754.1995.tb00723.x.

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43

Li, Haipeng, and Piret Plink‐Björklund. "Applying Information Theory and Bayesian Inference to Paleoenvironmental Interpretation." Geophysical Research Letters 46, no. 24 (December 17, 2019): 14477–85. http://dx.doi.org/10.1029/2019gl085928.

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44

Nguyen, Tuan V. "Pharmacogenetics of anti-resorptive therapy efficacy: a Bayesian interpretation." Osteoporosis International 16, no. 8 (February 1, 2005): 857–60. http://dx.doi.org/10.1007/s00198-004-1807-y.

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45

Lozada Aguilar, Miguel Ángel, Andrei Khrennikov, Klaudia Oleschko, and María de Jesús Correa. "Quantum Bayesian perspective for intelligence reservoir characterization, monitoring and management." Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 375, no. 2106 (October 2, 2017): 20160398. http://dx.doi.org/10.1098/rsta.2016.0398.

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Анотація:

The paper starts with a brief review of the literature about uncertainty in geological, geophysical and petrophysical data. In particular, we present the viewpoints of experts in geophysics on the application of Bayesian inference and subjective probability. Then we present arguments that the use of classical probability theory (CP) does not match completely the structure of geophysical data. We emphasize that such data are characterized by contextuality and non-Kolmogorovness (the impossibility to use the CP model), incompleteness as well as incompatibility of some geophysical measurements. These characteristics of geophysical data are similar to the characteristics of quantum physical data. Notwithstanding all this, contextuality can be seen as a major deviation of quantum theory from classical physics. In particular, the contextual probability viewpoint is the essence of the Växjö interpretation of quantum mechanics. We propose to use quantum probability (QP) for decision-making during the characterization, modelling, exploring and management of the intelligent hydrocarbon reservoir . Quantum Bayesianism (QBism), one of the recently developed information interpretations of quantum theory, can be used as the interpretational basis for such QP decision-making in geology, geophysics and petroleum projects design and management. This article is part of the themed issue ‘Second quantum revolution: foundational questions’.

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46

Berry, Donald A. "Introduction to Bayesian methods III: use and interpretation of Bayesian tools in design and analysis." Clinical Trials: Journal of the Society for Clinical Trials 2, no. 4 (August 2005): 295–300. http://dx.doi.org/10.1191/1740774505cn100oa.

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47

Marsman, Maarten, and Eric-Jan Wagenmakers. "Three Insights from a Bayesian Interpretation of the One-Sided P Value." Educational and Psychological Measurement 77, no. 3 (October 5, 2016): 529–39. http://dx.doi.org/10.1177/0013164416669201.

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Анотація:

P values have been critiqued on several grounds but remain entrenched as the dominant inferential method in the empirical sciences. In this article, we elaborate on the fact that in many statistical models, the one-sided P value has a direct Bayesian interpretation as the approximate posterior mass for values lower than zero. The connection between the one-sided P value and posterior probability mass reveals three insights: (1) P values can be interpreted as Bayesian tests of direction, to be used only when the null hypothesis is known from the outset to be false; (2) as a measure of evidence, P values are biased against a point null hypothesis; and (3) with N fixed and effect size variable, there is an approximately linear relation between P values and Bayesian point null hypothesis tests.

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48

Bayliss, Alex, Don Benson, Dawn Galer, Louise Humphrey, Lesley McFadyen, and Alasdair Whittle. "One Thing After Another: the Date of the Ascott-under-Wychwood Long Barrow." Cambridge Archaeological Journal 17, S1 (January 30, 2007): 29–44. http://dx.doi.org/10.1017/s0959774307000157.

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Анотація:

Forty-four radiocarbon results are now available from the Ascott-under-Wychwood long barrow, and are presented within an interpretive Bayesian statistical framework. Three alternative archaeological interpretations of the sequence are given, each with a separate Bayesian model. In our preferred model, pre-barrow occupation including small timber structures and a midden was followed by a gap long enough to allow a turfline to form. Cists and the primary barrow were then initiated and the first human remains inserted into the cists; there was subsequently a secondary extension to the barrow. In the Bayesian model for this interpretation, occupation goes back to the fortieth century cal. bc, the midden being quite short-lived in the latter part of the fortieth or first part of the thirty-ninth century cal. bc. The gap was very probably not less than 50 years long, in the latter part of the thirty-ninth century cal. bc and the first half of the thirty-eighth century cal. bc. The barrow was begun between 3760–3695 cal. bc and extended in 3745–3670 cal. bc, probably within a generation. The first bodies were inserted in 3755–3690 cal. bc, contemporaneously with the primary barrow, and the last remains were probably deposited in the 3640s or 3630s cal. bc. The use of the monument probably did not exceed three to five generations. In an alternative interpretation of the sequence, greater continuity is seen between the underlying timber structures and midden on the one hand and the cists on the other, which could have preceded the initiation of the barrow itself. The Bayesian model for this interpretation suggests the gap between occupation and barrow was much shorter, probably of only 1–40 years' duration. It gives slightly different other estimates for the sequence but agrees with the main model in suggesting an overall short span of use for the whole monument. In a third interpretation, some of the human remains are interpreted as older than the cists and barrow. The Bayesian model for this again gives slightly different estimates but suggests that such putatively ancestral remains would not have been more than a decade or two older than the initiation of cists and barrow. Results are briefly discussed in relation to the overall sequence from occupation and midden to monument, the brevity of monument use, and issues of remembrance.

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49

Whittle, Alasdair, Alex Bayliss, and Michael Wysocki. "Once in a Lifetime: the Date of the Wayland's Smithy Long Barrow." Cambridge Archaeological Journal 17, S1 (January 30, 2007): 103–21. http://dx.doi.org/10.1017/s0959774307000194.

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Анотація:

Twenty-three radiocarbon results are now available from the Wayland's Smithy long barrow, and are presented within an interpretive Bayesian statistical framework. Four alternative archaeological interpretations of the sequence are considered, each with a separate Bayesian model, though only two are presented in detail. The differences are based on different readings of the sequence of Wayland's Smithy II. In our preferred interpretation of the sequence, the primary mortuary structure was some kind of lidded wooden box, accessible for deposition over a period of time, and then closed by the mound of Wayland's Smithy I; Wayland's Smithy II was a unitary construction, with transepted chambers, secondary kerb and secondary ditches all constructed together. In the Bayesian model for this interpretation, deposition began in the earlier thirty-sixth century cal. bc, and probably lasted for a generation. A gap of probably 40–100 years ensued, before the first small mound was constructed in 3520–3470 cal. bc. After another gap, probably of only 1–35 years, the second phase of the monument was probably constructed in the middle to later part of the thirty-fifth century cal. bc (3460–3400 cal. bc), and its use probably extended to the middle decades of the thirty-fourth century cal. bc. Results are discussed in relation to the local setting, the nature of mortuary rites and the creation of tradition.

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50

Miočević, Milica. "A Tutorial in Bayesian Mediation Analysis With Latent Variables." Methodology 15, no. 4 (October 1, 2019): 137–46. http://dx.doi.org/10.1027/1614-2241/a000177.

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Анотація:

Abstract. Maximum Likelihood (ML) estimation is a common estimation method in Structural Equation Modeling (SEM), and parameters in such analyses are interpreted using frequentist terms and definition of probability. It is also possible, and sometimes more advantageous ( Lee & Song, 2004 ; Rindskopf, 2012 ), to fit structural equation models in the Bayesian framework ( Kaplan & Depaoli, 2012 ; Levy & Choi, 2013 ; Scheines, Hoijtink, & Boomsma, 1999 ). Bayesian mediation analysis has been described for manifest variable models ( Enders, Fairchild, & MacKinnon, 2013 ; Yuan & MacKinnon, 2009 ). This tutorial outlines considerations in the analysis and interpretation of results for the single mediator model with latent variables. The reader is guided through model specification, estimation, and the interpretations of results obtained using two kinds of diffuse priors and one set of informative priors. Recommendations are made for applied researchers and annotated syntax is provided in R2OpenBUGS and Mplus. The target audience for this article are researchers wanting to learn how to fit the single mediator model as a Bayesian SEM.

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