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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.
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.
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.
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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.
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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.
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.
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Warrington, Gregory S. "Juggling Probabilities." American Mathematical Monthly 112, no. 2 (February 1, 2005): 105. http://dx.doi.org/10.2307/30037409.
Piesse, Andrea Robyn. "Coherent predictive probabilities." Thesis, University of Canterbury. Mathematics and Statistics, 1996. http://hdl.handle.net/10092/8049.
The main aim of this thesis is to study ways of predicting the outcome of a vector of category counts from a particular group, in the presence of like data from other groups regarded exchangeably with this one and with each other. The situation is formulated using the subjectivist framework and strategies for estimating these predictive probabilities are presented and analysed with regard to their coherency. The range of estimation procedures considered covers naive, empirical Bayes and hierarchical Bayesian methods. Surprisingly, it turns out that some of these strategies must be asserted with zero probability of being used, in order for them to be coherent. A theory is developed which proves to be very useful in determining whether or not this is the case for a given collection of predictive probabilities. The conclusion is that truly Bayesian inference may lie behind all of the coherent strategies discovered, even when they are proposed under the guise of some other motivation.
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Beebee, Helen. "Causes and probabilities." Thesis, King's College London (University of London), 1996. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.244728.
Prediction intervals for class probabilities are of interest in machine learning because they can quantify the uncertainty about the class probability estimate for a test instance. The idea is that all likely class probability values of the test instance are included, with a pre-specified confidence level, in the calculated prediction interval. This thesis proposes a probabilistic model for calculating such prediction intervals. Given the unobservability of class probabilities, a Bayesian approach is employed to derive a complete distribution of the class probability of a test instance based on a set of class observations of training instances in the neighbourhood of the test instance. A random decision tree ensemble learning algorithm is also proposed, whose prediction output constitutes the neighbourhood that is used by the Bayesian model to produce a PI for the test instance. The Bayesian model, which is used in conjunction with the ensemble learning algorithm and the standard nearest-neighbour classifier, is evaluated on artificial datasets and modified real datasets.
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Roy, Kirk Andrew. "Laplace transforms, probabilities and queues." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1997. http://www.collectionscanada.ca/obj/s4/f2/dsk2/ftp01/MQ31000.pdf.
Peña-Castillo, Maria de Lourdes. "Probabilities and simulations in poker." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1999. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape7/PQDD_0018/MQ47080.pdf.
老瑞欣 and Sui-yan Victor Lo. "Statistical modelling of gambling probabilities." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 1992. http://hub.hku.hk/bib/B3123270X.
Cooper, Iain E. "Surface reactions and sticking probabilities." Thesis, University of Nottingham, 2004. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.403696.
This dissertation follows, scrupulously, the probability of default model used by the National University of Singapore Risk Management Institute (NUS-RMI). Any deviations or omissions are noted with reasons related to the scope of this study on modelling probabilities of corporate default of South African firms. Using our model, we simulate defaults and subsequently, infer parameters using classical statistical frequentist likelihood estimation and one-world-view pseudo-likelihood estimation. We improve the initial estimates from our pseudo-likelihood estimation by using Sequential Monte Carlo techniques and pseudo-Bayesian inference. With these techniques, we significantly improve upon our original parameter estimates. The increase in accuracy is most significant when using few samples which mimics real world data availability
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Lo, Sui-yan Victor. "Statistical modelling of gambling probabilities /." [Hong Kong] : University of Hong Kong, 1992. http://sunzi.lib.hku.hk/hkuto/record.jsp?B13205389.
Richter, Michael M., and Rosina O. Weber. "Probabilities." In Case-Based Reasoning, 357–72. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-40167-1_16.
Schay, Géza. "Probabilities." In Introduction to Probability with Statistical Applications, 53–103. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-30620-9_4.
Stepanov, Sergey S. "Probabilities." In Stochastic World, 89–108. Heidelberg: Springer International Publishing, 2013. http://dx.doi.org/10.1007/978-3-319-00071-8_4.
Brandt, Siegmund. "Probabilities." In Data Analysis, 7–13. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-03762-2_2.
Müller, Tibor, and Harmund Müller. "Probabilities." In Modelling in Natural Sciences, 195–227. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-662-05304-1_7.
Michaud, Michael A. G. "Probabilities." In Contact with Alien Civilizations, 54–57. New York, NY: Springer New York, 2007. http://dx.doi.org/10.1007/978-0-387-68618-9_6.
Kjærulff, Uffe B., and Anders L. Madsen. "Probabilities." In Information Science and Statistics, 39–67. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4614-5104-4_3.
Тези доповідей конференцій з теми "Probabilities":
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Kuosmanen, Pauli, and Jaakko T. Astola. "Selection probabilities." In SPIE's 1993 International Symposium on Optics, Imaging, and Instrumentation, edited by Edward R. Dougherty, Paul D. Gader, and Jean C. Serra. SPIE, 1993. http://dx.doi.org/10.1117/12.146661.
Caticha, Ariel, and Adom Giffin. "Updating Probabilities." In Bayesian Inference and Maximum Entropy Methods In Science and Engineering. AIP, 2006. http://dx.doi.org/10.1063/1.2423258.
Yang, Suwen, and Mark Greenstreet. "Computing Synchronizer Failure Probabilities." In Design, Automation & Test in Europe Conference. IEEE, 2007. http://dx.doi.org/10.1109/date.2007.364487.
Zadeh, Lotfi A. "Computation with imprecise probabilities." In 2008 IEEE International Conference on Information Reuse and Integration. IEEE, 2008. http://dx.doi.org/10.1109/iri.2008.4582989.
Bauder, Richard A., and Taghi M. Khoshgoftaar. "Estimating Outlier Score Probabilities." In 2017 IEEE International Conference on Information Reuse and Integration (IRI). IEEE, 2017. http://dx.doi.org/10.1109/iri.2017.19.
Zimmerman, Peter D. "The impossibility of probabilities." In NUCLEAR WEAPONS AND RELATED SECURITY ISSUES. Author(s), 2017. http://dx.doi.org/10.1063/1.5009233.
Kogelnik, Herwig, and Peter J. Winzer. "PMD Outage Probabilities Revisited." In OFC/NFOEC 2007 - 2007 Conference on Optical Fiber Communication and the National Fiber Optic Engineers Conference. IEEE, 2007. http://dx.doi.org/10.1109/ofc.2007.4348833.
KHRENNIKOV, ANDREI. "ORIGIN OF QUANTUM PROBABILITIES." In Proceedings of the Conference. WORLD SCIENTIFIC, 2001. http://dx.doi.org/10.1142/9789812810809_0014.
Zhang, Shutao, and Zhizheng Zhang. "Epistemic Specifications with Probabilities." In 2017 IEEE 29th International Conference on Tools with Artificial Intelligence (ICTAI). IEEE, 2017. http://dx.doi.org/10.1109/ictai.2017.00194.
Kyburg Jr, Henry E. Objective Probabilities. Fort Belvoir, VA: Defense Technical Information Center, January 1989. http://dx.doi.org/10.21236/ada250601.
Kyburg, Henry, and Jr. Higher Order Probabilities. Fort Belvoir, VA: Defense Technical Information Center, January 1988. http://dx.doi.org/10.21236/ada250537.
Hamm, Robert M. People Misinterpret Conditional Probabilities. Fort Belvoir, VA: Defense Technical Information Center, January 1995. http://dx.doi.org/10.21236/ada293527.
Khemlani, Sangeet S., Max Lotstein, and Phil Johnson-Laird. The Probabilities of Unique Events. Fort Belvoir, VA: Defense Technical Information Center, August 2012. http://dx.doi.org/10.21236/ada568559.
Evans, M., Z. Govindarajulu, and J. Barthoulot. Estimates of Circular Error Probabilities. Fort Belvoir, VA: Defense Technical Information Center, December 1985. http://dx.doi.org/10.21236/ada163257.
Iyengar, Satish. Importance Sampling for Tail Probabilities. Fort Belvoir, VA: Defense Technical Information Center, February 1991. http://dx.doi.org/10.21236/ada232412.
Gibbons, Robert D., R. D. Bock, and Donald Hedeker. Approximating Multivariate Normal Orthant Probabilities. Fort Belvoir, VA: Defense Technical Information Center, June 1990. http://dx.doi.org/10.21236/ada229129.
Brodsky, Stanley J. Structure Functions Are Not Parton Probabilities. Office of Scientific and Technical Information (OSTI), April 2001. http://dx.doi.org/10.2172/784914.
Ketseoglou, Thomas, and Evaggelos Geraniotis. Multireception Probabilities for FH/SSMA Communications. Fort Belvoir, VA: Defense Technical Information Center, January 1989. http://dx.doi.org/10.21236/ada454840.
Parke, Stephen J., and Mark D. Messier. Cross Check of NOvA Oscillation Probabilities. Office of Scientific and Technical Information (OSTI), January 2018. http://dx.doi.org/10.2172/1418447.
