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Статті в журналах з теми "Probabilities"

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Автор: Grafiati

Опубліковано: 4 червня 2021

Оновлено: 11 вересня 2023

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1

Mugur-Sch�chter, Mioara. "Quantum probabilities, Kolmogorov probabilities, and informational probabilities." International Journal of Theoretical Physics 33, no. 1 (January 1994): 53–90. http://dx.doi.org/10.1007/bf00671614.

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2

Yukalov, Vyacheslav, and Didier Sornette. "Quantum Probabilities as Behavioral Probabilities." Entropy 19, no. 3 (March 13, 2017): 112. http://dx.doi.org/10.3390/e19030112.

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3

Czeiszperger, Michael, and Stephen Jeske. "Probabilities." Computer Music Journal 14, no. 2 (1990): 68. http://dx.doi.org/10.2307/3679716.

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4

Fang, Di, Jenny Chong, and Jeffrey R. Wilson. "Predicted Probabilities' Relationship to Inclusion Probabilities." American Journal of Public Health 105, no. 5 (May 2015): 837–39. http://dx.doi.org/10.2105/ajph.2015.302592.

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5

Hájek, Alan. "Probabilities of counterfactuals and counterfactual probabilities." Journal of Applied Logic 12, no. 3 (September 2014): 235–51. http://dx.doi.org/10.1016/j.jal.2013.11.001.

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6

Lewis, David. "Probabilities of Conditionals and Conditional Probabilities II." Philosophical Review 95, no. 4 (October 1986): 581. http://dx.doi.org/10.2307/2185051.

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7

Barmpalias, George, and Andrew Lewis-Pye. "Computing halting probabilities from other halting probabilities." Theoretical Computer Science 660 (January 2017): 16–22. http://dx.doi.org/10.1016/j.tcs.2016.11.013.

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8

Fréché, Jean-Pierre. "Des probabilités négatives ?" Revue des questions scientifiques 193, no. 1-2 (January 1, 2022): 49–68. http://dx.doi.org/10.14428/qs.v193i1-2.70203.

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Анотація:

Jusqu’en 1932, les probabilités, tant en mathématique qu’en physique, étaient positives. Mais cette année-là, Wigner publia un article qui introduisait en physique statistique quantique une distribution de probabilités prenant aussi bien des valeurs négatives que des valeurs positives. Le texte qui suit établit d’abord brièvement un parallèle entre l’avènement des nombres négatifs et des nombres complexes au XVIe siècle d’une part, et l’avènement des probabilités négatives au XXe siècle d’autre part. Puis il décrit un « dispositif de pensée » qui propose des probabilités positives et négatives ; il en donne une critique. Ensuite, il expose une expérience plus réelle — diffusion de particules le long d’une tige infinie — qui fait apparaître des probabilités négatives et spécifie le type d’événements auxquelles elles sont attachées dans ce cas. Une comparaison est faite avec le « dispositif de pensée ». Enfin, il explique en quoi la distribution de Wigner étend à la mécanique quantique la distribution classique de Liouville attachée à l’espace de phase de la physique statistique classique. Il conclut en décrivant les pistes sur lesquelles s’est engagée la recherche dans le domaine des probabilités négatives et revient sur le parallèle initialement établi avec les nombres négatifs et les nombres complexes. * * * Until 1932, in both mathematics and physics, probabilities were positive. In the course of that year, however, Wigner published an article that introduced a probability distribution which incorporated negative values along with positive ones into quantum statistical physics. The present article opens by drawing a succinct parallel between the emergence of negative numbers and complex numbers in the 16th century, on the one hand, and the advent of negative probabilities in the 20th century, on the other hand. It then goes on to describe a “thought model” offering positive and negative probabilities, which is evaluated. Next, a more concrete experiment is addressed — the diffusion of particles along an infinite line —, which reveals the negative probabilities and specifies to which events these are linked in this particular case. This experiment is then compared to the “thought model”. Lastly, an explanation of how the Wigner distribution extends to quantum mechanics, through the standard Liouville distribution associated with the phase space of mainstream statistical physics, is presented. The conclusion expounds upon the various paths of research within the field of negative probabilities, and revisits the initial parallel established between negative numbers and complex numbers.

9

Grunwald, P. D., and J. Y. Halpern. "Updating Probabilities." Journal of Artificial Intelligence Research 19 (October 1, 2003): 243–78. http://dx.doi.org/10.1613/jair.1164.

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As examples such as the Monty Hall puzzle show, applying conditioning to update a probability distribution on a ``naive space'', which does not take into account the protocol used, can often lead to counterintuitive results. Here we examine why. A criterion known as CAR (``coarsening at random'') in the statistical literature characterizes when ``naive'' conditioning in a naive space works. We show that the CAR condition holds rather infrequently, and we provide a procedural characterization of it, by giving a randomized algorithm that generates all and only distributions for which CAR holds. This substantially extends previous characterizations of CAR. We also consider more generalized notions of update such as Jeffrey conditioning and minimizing relative entropy (MRE). We give a generalization of the CAR condition that characterizes when Jeffrey conditioning leads to appropriate answers, and show that there exist some very simple settings in which MRE essentially never gives the right results. This generalizes and interconnects previous results obtained in the literature on CAR and MRE.

10

Warrington, Gregory S. "Juggling Probabilities." American Mathematical Monthly 112, no. 2 (February 1, 2005): 105. http://dx.doi.org/10.2307/30037409.

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11

McVey, Jack L., Anthony Kettaneh, Tibor R. Machan, John Bryant, and Alfred R. Beronio. "Future Probabilities." Science News 135, no. 9 (March 4, 1989): 131. http://dx.doi.org/10.2307/3973594.

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12

Jeffrey, Richard. "Unknown probabilities." Erkenntnis 45, no. 2-3 (November 1996): 327–35. http://dx.doi.org/10.1007/bf00276797.

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13

Pruss, A. R. "Conditional probabilities." Analysis 72, no. 3 (June 1, 2012): 488–91. http://dx.doi.org/10.1093/analys/ans076.

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14

Hansson, Sven Ove. "Past Probabilities." Notre Dame Journal of Formal Logic 51, no. 2 (April 2010): 207–23. http://dx.doi.org/10.1215/00294527-2010-013.

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15

Dassios, Angelos. "Ruin Probabilities." Journal of the American Statistical Association 97, no. 460 (December 2002): 1211–12. http://dx.doi.org/10.1198/jasa.2002.s243.

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16

Fletcher, J. "Conditional probabilities." BMJ 338, jan14 3 (January 14, 2009): b113. http://dx.doi.org/10.1136/bmj.b113.

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17

Benci, Vieri, Leon Horsten, and Sylvia Wenmackers. "Infinitesimal Probabilities." British Journal for the Philosophy of Science 69, no. 2 (June 1, 2018): 509–52. http://dx.doi.org/10.1093/bjps/axw013.

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18

Litwiller, Bonnie H., and David R. Duncan. "Keno Probabilities." School Science and Mathematics 87, no. 1 (January 1987): 33–39. http://dx.doi.org/10.1111/j.1949-8594.1987.tb17216.x.

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19

Warrington, Gregory S. "Juggling Probabilities." American Mathematical Monthly 112, no. 2 (February 2005): 105–18. http://dx.doi.org/10.1080/00029890.2005.11920175.

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20

Roon, Hannah. "Statistical probabilities." Pigment & Resin Technology 16, no. 6 (June 1987): 4. http://dx.doi.org/10.1108/eb042366.

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21

Hartigan, J. A., and T. B. Murphy. "Inferred probabilities." Journal of Statistical Planning and Inference 105, no. 1 (June 2002): 23–34. http://dx.doi.org/10.1016/s0378-3758(01)00202-6.

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22

DE COOMAN, GERT. "Imprecise probabilities." Risk Decision and Policy 5, no. 2 (June 2000): 107–9. http://dx.doi.org/10.1017/s135753090000017x.

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23

Jonah, Charles D. "Cheating Probabilities." Journal of Chemical Education 75, no. 9 (September 1998): 1089. http://dx.doi.org/10.1021/ed075p1089.3.

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24

George, Glyn. "Parallel probabilities." Mathematical Gazette 104, no. 560 (June 18, 2020): 271–80. http://dx.doi.org/10.1017/mag.2020.50.

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Анотація:

After several years of teaching an introduction to probability and statistics for engineering degree students, my attention has been captured by some variations on the familiar general addition law of probability. Network analysis of components connected in parallel is one of many applications.

25

Milne, Peter. "Physical probabilities." Synthese 73, no. 2 (November 1987): 329–59. http://dx.doi.org/10.1007/bf00484746.

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26

Maas, Kees, Marco Steenbergen, and Willem Saris. "Vote probabilities." Electoral Studies 9, no. 2 (June 1990): 91–107. http://dx.doi.org/10.1016/0261-3794(90)90002-p.

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27

Skilling, John. "Prior probabilities." Synthese 63, no. 1 (April 1985): 1–34. http://dx.doi.org/10.1007/bf00485953.

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28

Drossos, C. A., and P. L. Theodoropoulos. "-fuzzy probabilities." Fuzzy Sets and Systems 78, no. 3 (March 1996): 355–69. http://dx.doi.org/10.1016/0165-0114(96)84617-3.

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29

Uzhga-Rebrov, O. "UNCERTAIN PROBABILITIES." Environment. Technology. Resources. Proceedings of the International Scientific and Practical Conference 1 (June 26, 2006): 377. http://dx.doi.org/10.17770/etr2003vol1.2020.

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Анотація:

The uncertainty of probabilistic evaluations results from the lack of sufficient information and/or knowledge underlying those random events. Uncertainty representation in the form of second order probability distribution or interval evaluations does not cause any objections from the theoretical point of view. On the other hand, what is worthy in the second order probabilities is that they allow one to model a real uncertainty of subjective probabilistic evaluations resulting from the lack of information and/or knowledge. Processing of uncertain information regarding probabilistic evaluations can help make a validated decision about the collection of additional information aimed to remove completely or to reduce the existing uncertainty.

30

Pharoah, P. D. P. "Balancing probabilities." BMJ 342, may17 2 (May 17, 2011): d3048. http://dx.doi.org/10.1136/bmj.d3048.

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31

Dubucs, J. "Embedded probabilities." Theory and Decision 30, no. 3 (May 1991): 279–84. http://dx.doi.org/10.1007/bf00132448.

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32

Viscusi, W. Kip, and William N. Evans. "Behavioral Probabilities." Journal of Risk and Uncertainty 32, no. 1 (January 2006): 5–15. http://dx.doi.org/10.1007/s10797-006-6663-6.

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33

Easton, Kristen L. "Distinct Probabilities." Rehabilitation Nursing 19, no. 5 (September 10, 1994): 303–4. http://dx.doi.org/10.1002/j.2048-7940.1994.tb00828.x.

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34

Gorski, Andrew. "Chi-Square Probabilities are Poisson Probabilities in Disguise." IEEE Transactions on Reliability R-34, no. 3 (August 1985): 209–11. http://dx.doi.org/10.1109/tr.1985.5222117.

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35

Khrennikov, Andrei. "CHSH Inequality: Quantum Probabilities as Classical Conditional Probabilities." Foundations of Physics 45, no. 7 (November 9, 2014): 711–25. http://dx.doi.org/10.1007/s10701-014-9851-8.

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36

Li, Xiaoou, and Jingchen Liu. "Rare-event simulation and efficient discretization for the supremum of Gaussian random fields." Advances in Applied Probability 47, no. 3 (September 2015): 787–816. http://dx.doi.org/10.1239/aap/1444308882.

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In this paper we consider a classic problem concerning the high excursion probabilities of a Gaussian random fieldfliving on a compact setT. We develop efficient computational methods for the tail probabilitiesℙ{supTf(t) >b}. For each positive ε, we present Monte Carlo algorithms that run inconstanttime and compute the probabilities with relative error ε for arbitrarily largeb. The efficiency results are applicable to a large class of Hölder continuous Gaussian random fields. Besides computations, the change of measure and its analysis techniques have several theoretical and practical indications in the asymptotic analysis of Gaussian random fields.

37

Li, Xiaoou, and Jingchen Liu. "Rare-event simulation and efficient discretization for the supremum of Gaussian random fields." Advances in Applied Probability 47, no. 03 (September 2015): 787–816. http://dx.doi.org/10.1017/s0001867800048837.

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In this paper we consider a classic problem concerning the high excursion probabilities of a Gaussian random fieldfliving on a compact setT. We develop efficient computational methods for the tail probabilitiesℙ{supTf(t) >b}. For each positive ε, we present Monte Carlo algorithms that run inconstanttime and compute the probabilities with relative error ε for arbitrarily largeb. The efficiency results are applicable to a large class of Hölder continuous Gaussian random fields. Besides computations, the change of measure and its analysis techniques have several theoretical and practical indications in the asymptotic analysis of Gaussian random fields.

38

Dhami, Mandeep K., and Thomas S. Wallsten. "Interpersonal comparison of subjective probabilities: Toward translating linguistic probabilities." Memory & Cognition 33, no. 6 (September 2005): 1057–68. http://dx.doi.org/10.3758/bf03193213.

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39

Rédel, Miklós. "Quantum conditional probabilities are not probabilities of quantum conditional." Physics Letters A 139, no. 7 (August 1989): 287–90. http://dx.doi.org/10.1016/0375-9601(89)90454-4.

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40

Suppes, Patrick, and Mario Zanotti. "Conditions on upper and lower probabilities to imply probabilities." Erkenntnis 31, no. 2-3 (September 1989): 323–45. http://dx.doi.org/10.1007/bf01236568.

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41

Bartoszek, Wojciech. "On concentrated probabilities." Annales Polonici Mathematici 61, no. 1 (1995): 25–38. http://dx.doi.org/10.4064/ap-61-1-25-38.

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42

Papineau, David. "Probabilities and Causes." Journal of Philosophy 82, no. 2 (February 1985): 57. http://dx.doi.org/10.2307/2026555.

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43

Widen, Holly M., James B. Elsner, Rizalino B. Cruz, Guang Xing, Erik Fraza, Loury Migliorelli, Sarah Strazzo, et al. "Adjusted Tornado Probabilities." E-Journal of Severe Storms Meteorology 8, no. 7 (October 5, 2021): 1–12. http://dx.doi.org/10.55599/ejssm.v8i7.52.

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Анотація:

Tornado occurrence rates computed from the available reports are biased low relative to the unknown true rates. To correct for this low bias, the authors demonstrate a method to estimate the annual probability of being struck by a tornado that uses the average report density estimated as a function of distance from nearest city/town center. The method is demonstrated on Kansas and then applied to 15 other tornado-prone states from Nebraska to Tennessee. States are ranked according to their adjusted tornado rate and comparisons are made with raw rates published elsewhere. The adjusted rates, expressed as return periods, are <1250 y for four states, including Alabama, Mississippi, Arkansas, and Oklahoma. The expected annual number of people exposed to tornadoes is highest for Illinois followed by Alabama and Indiana. For the four states with the highest tornado rates, exposure increases since 1980 are largest for Oklahoma (24%) and Alabama (23%).

44

Page, Don N. "Possibilities for probabilities." Journal of Cosmology and Astroparticle Physics 2022, no. 10 (October 1, 2022): 023. http://dx.doi.org/10.1088/1475-7516/2022/10/023.

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Abstract In ordinary situations involving a small part of the universe, Born's rule seems to work well for calculating probabilities of observations in quantum theory. However, there are a number of reasons for believing that it is not adequate for many cosmological purposes. Here a number of possible generalizations of Born's rule are discussed, explaining why they are consistent with the present statistical support for Born's rule in ordinary situations but can help solve various cosmological problems.

45

Drees, Holger, and Laurens de Haan. "Estimating failure probabilities." Bernoulli 21, no. 2 (May 2015): 957–1001. http://dx.doi.org/10.3150/13-bej594.

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46

Fletcher, Mike. "CALCULATING DEPENDENT PROBABILITIES." Mathematics Enthusiast 6, no. 1-2 (January 1, 2009): 91–94. http://dx.doi.org/10.54870/1551-3440.1137.

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47

Gottschalk, Lars. "Regional Exceedance Probabilities." Hydrology Research 20, no. 4-5 (August 1, 1989): 201–14. http://dx.doi.org/10.2166/nh.1989.0016.

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Construction of a regional flood frequency curve is based, as a rule, on fitting this curve to representative quantiles. In a regional sample of floods the probability of extreme values corresponding to return periods, that exceed the record lengths, is much larger than that of individual series, used to determine the representative quantiles. The probabilities of exceedance of regional extremes can be calculated straightforward in case of independent data, applying the theory of order statistics. For regionally dependent data one can define an equivalent number of independent regional series and then utilize the theory for independent data. This approach is exemplified with flood data from Norway.

48

Karni, Edi, and Zvi Safra. "Rank-Dependent Probabilities." Economic Journal 100, no. 401 (June 1990): 487. http://dx.doi.org/10.2307/2234135.

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49

Honda, Hidehito, and Kimihiko Yamagishi. "Directional Verbal Probabilities." Experimental Psychology 53, no. 3 (January 2006): 161–70. http://dx.doi.org/10.1027/1618-3169.53.3.161.

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Verbal probability expressions (e.g., it is possible or doubtful) convey not only vague numerical meanings (i.e., probability) but also semantic functions, called directionality. We performed two experiments to examine whether preferential judgments are consistent with numerical meanings of verbal probabilities regardless of directionality. The results showed that because of the effects of directionality, perceived degrees of certainty for verbal probabilities differed between a binary choice and a numerical translation (Experiment 1), and decisions based on a verbal probability do not correspond to those based on a numerical translation for verbal probabilities (Experiment 2). These findings suggest that directionality of verbal probabilities is an independent feature from numerical meanings; hence numerical meanings of verbal probability alone remain insufficient to explain the effects of directionality on preferential judgments.

50

Schmock, Uwe, and Michel Simonnet. "Measures and Probabilities." Journal of the American Statistical Association 93, no. 442 (June 1998): 840. http://dx.doi.org/10.2307/2670142.

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