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Artigos de revistas sobre o tema "Probability theory"

Autor: Grafiati

Publicado: 4 de junho de 2021

Última modificação: 9 de junho de 2025

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1

Thun, M. von. "Probability Theory and Probability Semantics." Australasian Journal of Philosophy 79, no. 4 (December 2001): 570–71. http://dx.doi.org/10.1080/713659287.

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2

Kiessler, Peter C. "Measure Theory and Probability Theory." Journal of the American Statistical Association 102, no. 479 (September 2007): 1078. http://dx.doi.org/10.1198/jasa.2007.s207.

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3

Berckmoes, B., R. Lowen, and J. Van Casteren. "Approach theory meets probability theory." Topology and its Applications 158, no. 7 (April 2011): 836–52. http://dx.doi.org/10.1016/j.topol.2011.01.004.

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4

Lindley, D. V., and Harold Jeffreys. "Theory of Probability." Mathematical Gazette 83, no. 497 (July 1999): 372. http://dx.doi.org/10.2307/3619118.

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5

Guionnet, Alice, Roland Speicher, and Dan-Virgil Voiculescu. "Free Probability Theory." Oberwolfach Reports 12, no. 2 (2015): 1571–629. http://dx.doi.org/10.4171/owr/2015/28.

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6

Guionnet, Alice, Roland Speicher, and Dan-Virgil Voiculescu. "Free Probability Theory." Oberwolfach Reports 15, no. 4 (December 16, 2019): 3147–215. http://dx.doi.org/10.4171/owr/2018/53.

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7

Bhat, B. R. "Modern Probability Theory." Biometrics 42, no. 4 (December 1986): 1007. http://dx.doi.org/10.2307/2530732.

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8

Jeffreys, H., P. A. P. Moran, and C. Chatfield. "Theory of Probability." Biometrics 41, no. 2 (June 1985): 597. http://dx.doi.org/10.2307/2530899.

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9

Speicher, Roland. "Free Probability Theory." Jahresbericht der Deutschen Mathematiker-Vereinigung 119, no. 1 (September 15, 2016): 3–30. http://dx.doi.org/10.1365/s13291-016-0150-5.

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10

MTW and Harold Jeffreys. "Theory of Probability." Journal of the American Statistical Association 94, no. 448 (December 1999): 1389. http://dx.doi.org/10.2307/2669965.

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11

RBL and Boris V. Gnedenko. "Theory of Probability." Journal of the American Statistical Association 94, no. 447 (September 1999): 990. http://dx.doi.org/10.2307/2670030.

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12

Heyer, H. "ALGEBRAIC PROBABILITY THEORY." Bulletin of the London Mathematical Society 22, no. 5 (September 1990): 523–24. http://dx.doi.org/10.1112/blms/22.5.523.

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13

Rédei, Miklós, and Stephen Jeffrey Summers. "Quantum probability theory." Studies in History and Philosophy of Science Part B: Studies in History and Philosophy of Modern Physics 38, no. 2 (June 2007): 390–417. http://dx.doi.org/10.1016/j.shpsb.2006.05.006.

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14

Diederich, Adele. "Projective probability theory." Journal of Mathematical Psychology 58 (January 2014): 55–57. http://dx.doi.org/10.1016/j.jmp.2013.12.003.

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15

Rota, Gian-Carlo. "Algebraic probability theory." Advances in Mathematics 84, no. 2 (December 1990): 272. http://dx.doi.org/10.1016/0001-8708(90)90050-w.

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16

Lane, John A., and J. Galambos. "Advanced Probability Theory." Applied Statistics 39, no. 2 (1990): 263. http://dx.doi.org/10.2307/2347769.

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17

Davy, P. J., and Janos Galambos. "Introductory Probability Theory." Applied Statistics 36, no. 3 (1987): 376. http://dx.doi.org/10.2307/2347800.

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18

Carlson, Roger, J. Galambos, M. A. Goldberg, J. G. Kalbfleisch, S. M. Ross, R. J. Larsen, and M. L. Marx. "Introductory Probability Theory." Journal of the American Statistical Association 81, no. 396 (December 1986): 1117. http://dx.doi.org/10.2307/2289097.

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19

Maslov, V. P. "Unbounded probability theory compatible with the probability theory of numbers." Mathematical Notes 91, no. 5-6 (May 2012): 697–703. http://dx.doi.org/10.1134/s0001434612050112.

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20

Erkinovna, Ergasheva Fatima, and Egamberdiyeva Mohinur Fakhriddin kizi. "OTHER METHODS OF TEACHING PROBABILITY THEORY AND COMBINATORICS." American Journal of Applied Sciences 6, no. 3 (March 1, 2024): 13–15. http://dx.doi.org/10.37547/tajas/volume06issue03-03.

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The traditional methods of teaching probability theory and combinatorics often fail to engage students and promote deep understanding. This study explores alternative teaching strategies that incorporate active learning, technology, and real-world applications to enhance student comprehension and interest in these mathematical fields. By comparing the effectiveness of these methods with conventional approaches, this research aims to provide educators with practical insights for improving mathematics education.

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21

Clarke, L. E., Malcolm Adams, and Victor Guillemin. "Measure Theory and Probability." Mathematical Gazette 71, no. 455 (March 1987): 80. http://dx.doi.org/10.2307/3616316.

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22

Rosalsky, Andrew, Malcolm Adams, and Victor Guillemin. "Measure Theory and Probability." Journal of the American Statistical Association 82, no. 398 (June 1987): 700. http://dx.doi.org/10.2307/2289512.

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23

Prewitt, Kathryn, and Richard Durrett. "Probability: Theory and Examples." Journal of the American Statistical Association 87, no. 418 (June 1992): 586. http://dx.doi.org/10.2307/2290301.

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24

RL, Malcolm Adams, and Victor Guillemin. "Measure Theory and Probability." Journal of the American Statistical Association 91, no. 436 (December 1996): 1754. http://dx.doi.org/10.2307/2291614.

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25

Durrett, R. "Probability: Theory and Examples." Biometrics 49, no. 3 (September 1993): 959. http://dx.doi.org/10.2307/2532227.

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26

Hirst, K. E., and E. A. Robinson. "Probability Theory and Application." Mathematical Gazette 70, no. 454 (December 1986): 321. http://dx.doi.org/10.2307/3616216.

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27

Kendall, Wilfrid S. "INTRODUCTION TO PROBABILITY THEORY." Bulletin of the London Mathematical Society 17, no. 6 (November 1985): 601–3. http://dx.doi.org/10.1112/blms/17.6.601.

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28

Rogers, L. C. G. "PROBABILITY: THEORY AND EXAMPLES." Bulletin of the London Mathematical Society 24, no. 2 (March 1992): 205–6. http://dx.doi.org/10.1112/blms/24.2.205.

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29

Kolmogorov, A. N. "Problems of Probability Theory." Theory of Probability & Its Applications 38, no. 2 (June 1994): 177–78. http://dx.doi.org/10.1137/1138020.

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30

Frigg, Roman, and Carl Hoefer. "Probability in GRW theory." Studies in History and Philosophy of Science Part B: Studies in History and Philosophy of Modern Physics 38, no. 2 (June 2007): 371–89. http://dx.doi.org/10.1016/j.shpsb.2006.12.002.

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31

Rota, Gian-Carlo. "Elements of probability theory." Advances in Mathematics 55, no. 1 (January 1985): 101. http://dx.doi.org/10.1016/0001-8708(85)90008-8.

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32

Drummond-Cole, Gabriel C., Jae-Suk Park, and John Terilla. "Homotopy probability theory I." Journal of Homotopy and Related Structures 10, no. 3 (November 19, 2013): 425–35. http://dx.doi.org/10.1007/s40062-013-0067-y.

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33

Drummond-Cole, Gabriel C., Jae-Suk Park, and John Terilla. "Homotopy probability theory II." Journal of Homotopy and Related Structures 10, no. 3 (April 6, 2014): 623–35. http://dx.doi.org/10.1007/s40062-014-0078-3.

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34

Athreya, K. B. "What is probability theory?" Resonance 20, no. 4 (April 2015): 292–310. http://dx.doi.org/10.1007/s12045-015-0186-3.

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35

Bru, Bernard. "Lagrange and probability theory." Lettera Matematica 2, no. 1-2 (April 3, 2014): 67–74. http://dx.doi.org/10.1007/s40329-014-0047-z.

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36

Schlechta, Karl. "Theory revision and probability." Notre Dame Journal of Formal Logic 32, no. 2 (March 1991): 307–19. http://dx.doi.org/10.1305/ndjfl/1093635755.

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37

Snell, J. Laurie, and Kiyosi Ito. "Introduction to Probability Theory." Journal of the American Statistical Association 81, no. 395 (September 1986): 857. http://dx.doi.org/10.2307/2289022.

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38

Mittal, Yashaswini, and M. M. Rao. "Probability Theory With Applications." Journal of the American Statistical Association 81, no. 394 (June 1986): 579. http://dx.doi.org/10.2307/2289275.

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39

Marletto, Chiara. "Constructor theory of probability." Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 472, no. 2192 (August 2016): 20150883. http://dx.doi.org/10.1098/rspa.2015.0883.

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Unitary quantum theory, having no Born Rule, is non-probabilistic . Hence the notorious problem of reconciling it with the unpredictability and appearance of stochasticity in quantum measurements. Generalizing and improving upon the so-called ‘decision-theoretic approach’, I shall recast that problem in the recently proposed constructor theory of information— where quantum theory is represented as one of a class of superinformation theories , which are local , non-probabilistic theories conforming to certain constructor-theoretic conditions. I prove that the unpredictability of measurement outcomes (to which constructor theory gives an exact meaning) necessarily arises in superinformation theories. Then I explain how the appearance of stochasticity in (finitely many) repeated measurements can arise under superinformation theories. And I establish sufficient conditions for a superinformation theory to inform decisions (made under it) as if it were probabilistic, via a Deutsch–Wallace-type argument—thus defining a class of decision-supporting superinformation theories. This broadens the domain of applicability of that argument to cover constructor-theory compliant theories. In addition, in this version some of the argument's assumptions, previously construed as merely decision-theoretic, follow from physical properties expressed by constructor-theoretic principles.

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40

Currie, Iain D., and E. A. Robinson. "Probability Theory and Applications." Journal of the Royal Statistical Society. Series A (General) 149, no. 3 (1986): 278. http://dx.doi.org/10.2307/2981570.

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41

Kennedy, D. P., and Kiyosi Ito. "Introduction to Probability Theory." Journal of the Royal Statistical Society. Series A (General) 148, no. 3 (1985): 283. http://dx.doi.org/10.2307/2981977.

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42

REEVES, T. V. "A Theory of Probability." British Journal for the Philosophy of Science 39, no. 2 (June 1, 1988): 161–82. http://dx.doi.org/10.1093/bjps/39.2.161.

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43

JANSONS, KALVIS M., and L. C. G. ROGERS. "Probability and dispersion theory." IMA Journal of Applied Mathematics 55, no. 2 (1995): 149–62. http://dx.doi.org/10.1093/imamat/55.2.149.

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44

Maslov, V. P. "On unbounded probability theory." Mathematical Notes 92, no. 1-2 (July 2012): 59–63. http://dx.doi.org/10.1134/s0001434612070073.

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45

Spouge, John L. "Introduction to probability theory." Mathematical Biosciences 78, no. 2 (April 1986): 311. http://dx.doi.org/10.1016/0025-5564(86)90132-x.

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46

Bagwell, C. Bruce, Benjamin C. Hunsberger, Donald J. Herbert, Mark E. Munson, Beth L. Hill, Chris M. Bray, and Frederic I. Preffer. "Probability state modeling theory." Cytometry Part A 87, no. 7 (May 25, 2015): 646–60. http://dx.doi.org/10.1002/cyto.a.22687.

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47

Kolmogorov, A. N., and S. Natarajan. "The theory of probability." Resonance 3, no. 4 (April 1998): 103–12. http://dx.doi.org/10.1007/bf02834617.

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48

Shiryaev, A. N. "Chair of Probability Theory." Moscow University Mathematics Bulletin 79, no. 6 (December 2024): 343–50. https://doi.org/10.3103/s0027132225700111.

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49

Golomb, Solomon W. "Probability, information theory, and prime number theory." Discrete Mathematics 106-107 (September 1992): 219–29. http://dx.doi.org/10.1016/0012-365x(92)90549-u.

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50

Diecidue, Enrico, Haim Levy, and Moshe Levy. "Probability Dominance." Review of Economics and Statistics 102, no. 5 (December 2020): 1006–20. http://dx.doi.org/10.1162/rest_a_00890.

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The most commonly employed paradigms for decision making under risk are expected utility, prospect theory, and regret theory. We examine the simple heuristic of maximizing the probability of being ahead, which in some natural economic situations may be in contradiction to all three of the above fundamental paradigms. We test whether this heuristic, which we call probability dominance (PD), affects decisions under risk. We set up head-to-head situations where all preferences of a given class (expected utility, original or cumulative prospect theory, or regret theory) favor one alternative yet PD favors the other. Our experiments reveal that 49% of subjects' choices are aligned with PD in contradiction to any form of expected utility or prospect theory maximization; 73% are aligned with PD as opposed to preferences under risk aversion and under original and cumulative prospect theory preferences; and 68% to 76% are aligned with PD contradicting preferences under regret theory. We conclude that probability dominance substantially affects choices and should therefore be incorporated into decision-making models. We show that PD has significant economic consequences. The PD heuristic may have evolved through situations of winner-take-all competition.

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