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Journal articles on the topic 'Probability experiments'

Author: Grafiati

Published: 4 June 2021

Last updated: 4 February 2022

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1

Stoyer, M. A., J. B. Patin, J. M. Kenneally, et al. "Random probability analysis of recent 48Ca experiments." European Physical Journal A 25, S1 (2005): 595–97. http://dx.doi.org/10.1140/epjad/i2005-06-125-x.

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2

Beigie, Darin. "Mathematical Exploration: Probability Experiments with Shared Spreadsheets." Mathematics Teaching in the Middle School 15, no. 8 (2010): 486–91. http://dx.doi.org/10.5951/mtms.15.8.0486.

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Probability Experiments with Shared Spreadsheets Probability experiments illustrate how apparently random events are ultimately governed by the laws of probability. A large number of trials are usually necessary for experimental data to converge to a theoretical prediction. Having students electronically combine their data into a shared spreadsheet provides an efficient and powerful way to collectively analyze a large amount of information. In this activity, seventh-grade students use a shared spreadsheet to collaboratively investigate what happens when they roll a pair of dice. They compare t

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3

Hess, Karl. "Modeling experiments using quantum and Kolmogorov probability." Journal of Physics: Condensed Matter 20, no. 45 (2008): 454207. http://dx.doi.org/10.1088/0953-8984/20/45/454207.

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4

Imparato, A., and L. Peliti. "Work probability distribution in single-molecule experiments." Europhysics Letters (EPL) 69, no. 4 (2005): 643–49. http://dx.doi.org/10.1209/epl/i2004-10390-3.

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5

Carr, James R. "Order relation correction experiments for probability kriging." Mathematical Geology 26, no. 5 (1994): 605–21. http://dx.doi.org/10.1007/bf02089244.

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6

Bonini, Nicolao, and Michel Gonzalez. "Inconsistent Probability Estimates of a Hypothesis." Experimental Psychology 52, no. 1 (2005): 55–66. http://dx.doi.org/10.1027/1618-3169.52.1.55.

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Abstract. This paper studies consistency in the judged probability of a target hypothesis in lists of mutually exclusive nonexhaustive hypotheses. Specifically, it controls the role played by the support of displayed competing hypotheses and the relatedness between the target hypothesis and its alternatives. Three experiments are reported. In all experiments, groups of people were presented with a list of mutually exclusive nonexhaustive causes of a person’s death. In the first two experiments, they were asked to judge the probability of each cause as that of the person’s decease. In the third

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7

Kleijnen, Jack P. C., and Wen Shi. "Sequential probability ratio tests: conservative and robust." SIMULATION 97, no. 1 (2020): 33–43. http://dx.doi.org/10.1177/0037549720954916.

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Because computers (except for parallel computers) generate simulation outputs sequentially, we recommend sequential probability ratio tests (SPRTs) for the statistical analysis of these outputs. However, until now simulation analysts have ignored SPRTs. To change this situation, we review SPRTs for the simplest case; namely, the case of choosing between two hypothesized values for the mean simulation output. For this case, the classic SPRT of Wald (Wald A. Sequential tests of statistical hypotheses. Ann Math Stat 1945; 16: 117–186) allows general types of distribution, including normal distrib

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8

Munk, Axel. "Tchebycheff-Experiments." Statistics 31, no. 4 (1998): 289–324. http://dx.doi.org/10.1080/02331889808802642.

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9

Finch, P. D. "Bell's inequality, probability modelling and quantum correlation experiments." Journal of Applied Probability 25, A (1988): 139–50. http://dx.doi.org/10.2307/3214152.

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Problems associated with setting up a probability model which generates the quantum theoretical probabilities for the two spin 1/2 particle system are examined. Arguments which claim to show that such a model cannot be constructed within classical probability theory under the assumption of local singlet states are also considered. It is shown that the model then in question is not a probability model in the sense that term is used elsewhere in science. An alternative model is proposed and its bearing on the Einstein-Bohr debate is briefly discussed.

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10

Finch, P. D. "Bell's inequality, probability modelling and quantum correlation experiments." Journal of Applied Probability 25, A (1988): 139–50. http://dx.doi.org/10.1017/s0021900200040316.

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Problems associated with setting up a probability model which generates the quantum theoretical probabilities for the two spin 1/2 particle system are examined. Arguments which claim to show that such a model cannot be constructed within classical probability theory under the assumption of local singlet states are also considered. It is shown that the model then in question is not a probability model in the sense that term is used elsewhere in science. An alternative model is proposed and its bearing on the Einstein-Bohr debate is briefly discussed.

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11

Norberg, Espen. "COMPARISON OF STATISTICAL EXPERIMENTS WITH FILTERED PROBABILITY SPACES." Statistics & Risk Modeling 20, no. 1-4 (2002): 1–28. http://dx.doi.org/10.1524/strm.2002.20.14.1.

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12

Zemek, J., and S. Hucek. "Experiments to determine the escape probability of photoelectrons." Fresenius' Journal of Analytical Chemistry 363, no. 2 (1999): 156–59. http://dx.doi.org/10.1007/s002160051163.

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13

Doan, Tiffany, Ori Friedman, and Stephanie Denison. "Young Children Use Probability to Infer Happiness and the Quality of Outcomes." Psychological Science 31, no. 2 (2019): 149–59. http://dx.doi.org/10.1177/0956797619895282.

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Happiness with an outcome often depends on whether better or worse outcomes were initially more likely. In five experiments, we found that young children ( N = 620, Experiments 1–4) and adults ( N = 254, Experiment 5) used probability to infer emotions and assess outcome quality. In Experiments 1 and 2, 5- and 6-year-olds (but not 4-year-olds) inferred that an agent would be less happy with an outcome if a better outcome were initially more likely. In Experiment 3, 4- to 6-year-olds used probability to assess quality. These findings suggest a developmental lag between 4-year-olds’ assessments

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14

Littell, Ramon C., and R. G. Petersen. "Agricultural Field Experiments." Journal of the American Statistical Association 91, no. 434 (1996): 912. http://dx.doi.org/10.2307/2291693.

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15

Keen, A., J. T. N. M. Thissen, J. A. Hoekstra, and J. Jansen. "SUCCESSIVE MEASUREMENT EXPERIMENTS." Statistica Neerlandica 40, no. 4 (1986): 205–23. http://dx.doi.org/10.1111/j.1467-9574.1986.tb01201.x.

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16

Agha, M., and J. A. Cornell. "Experiments with Mixtures." Applied Statistics 41, no. 1 (1992): 215. http://dx.doi.org/10.2307/2347631.

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17

Sun Park, Hee, and Timothy Levine. "A probability model of accuracy in deception detection experiments." Communication Monographs 68, no. 2 (2001): 201–10. http://dx.doi.org/10.1080/03637750128059.

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18

Huang, Shih-Hao, and Mong-Na Lo Huang. "Robust designs for probability estimation in binary response experiments." Journal of Statistical Planning and Inference 154 (November 2014): 116–32. http://dx.doi.org/10.1016/j.jspi.2013.12.001.

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19

Wallace, M., R. Feres, G. Yablonsky, and A. Stern. "Explicit formulas for reaction probability in reaction-diffusion experiments." Computers & Chemical Engineering 125 (June 2019): 612–22. http://dx.doi.org/10.1016/j.compchemeng.2016.06.007.

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20

Draper, Norman R., and Friedrich Pukelsheim. "Optimal Design of Experiments." Journal of the American Statistical Association 89, no. 426 (1994): 713. http://dx.doi.org/10.2307/2290880.

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21

Ramamoorthi, R. V., and H. Heyer. "Theory of Statistical Experiments." Journal of the American Statistical Association 80, no. 390 (1985): 489. http://dx.doi.org/10.2307/2287936.

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22

Siebert, E. "Infinitely divisible statistical experiments." Metrika 33, no. 1 (1986): 348. http://dx.doi.org/10.1007/bf01894767.

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23

Kinz-Thompson, Colin D., Korak Kumar Ray, and Ruben L. Gonzalez. "Bayesian Inference: The Comprehensive Approach to Analyzing Single-Molecule Experiments." Annual Review of Biophysics 50, no. 1 (2021): 191–208. http://dx.doi.org/10.1146/annurev-biophys-082120-103921.

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Biophysics experiments performed at single-molecule resolution provide exceptional insight into the structural details and dynamic behavior of biological systems. However, extracting this information from the corresponding experimental data unequivocally requires applying a biophysical model. In this review, we discuss how to use probability theory to apply these models to single-molecule data. Many current single-molecule data analysis methods apply parts of probability theory, sometimes unknowingly, and thus miss out on the full set of benefits provided by this self-consistent framework. The

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24

Crosier, Ronald B. "Symmetry in mixture experiments." Communications in Statistics - Theory and Methods 20, no. 5-6 (1991): 1911–35. http://dx.doi.org/10.1080/03610929108830608.

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25

Bailey, R. A. "Strata for Randomized Experiments." Journal of the Royal Statistical Society: Series B (Methodological) 53, no. 1 (1991): 27–66. http://dx.doi.org/10.1111/j.2517-6161.1991.tb01808.x.

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26

Heim, Pascal, Michael Rumetshofer, Bernhard Thaler, Wolfgang E. Ernst, Wolfgang von der Linden, and Markus Koch. "Bayesian probability theory to identify false coincidences in coincidence experiments." EPJ Web of Conferences 205 (2019): 09025. http://dx.doi.org/10.1051/epjconf/201920509025.

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We describe a Bayesian formalism to analyse femtosecond pump-probe photoionization experiments with photoelectron-photoion coincidence (PEPICO) detection. This approach overcomes the drawback of extraordinary long data acquisition times of PEPICO detection. In extension to simply excluding false coincidences as previously [1], we here present an investigation of their influence on the underlying spectrum. The software is provided at https://github.com/fslab-tugraz/PEPICOBayes/.

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27

Lee, S. H., H. S. Choi, and B. M. Kwak. "Multilevel design of experiments for statistical moment and probability calculation." Structural and Multidisciplinary Optimization 37, no. 1 (2008): 57–70. http://dx.doi.org/10.1007/s00158-007-0215-2.

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28

Mather, D., C. A. Greated, and I. G. Bryden. "Experiments on the probability of wave breaking in random seas." Applied Ocean Research 14, no. 1 (1992): 11–21. http://dx.doi.org/10.1016/0141-1187(92)90003-3.

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29

Liu, Chia-Lun, Hui-Yan Chiau, Philip Tseng, et al. "Antisaccade Cost Is Modulated by Contextual Experience of Location Probability." Journal of Neurophysiology 103, no. 3 (2010): 1438–47. http://dx.doi.org/10.1152/jn.00815.2009.

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It is well known that pro- and antisaccades may deploy different cognitive processes. However, the specific reason why antisaccades have longer latencies than prosaccades is still under debate. In three experiments, we studied the factors contributing to the antisaccade cost by taking attentional orienting and target location probabilities into account. In experiment 1, using a new antisaccade paradigm, we directly tested Olk and Kingstone's hypothesis, which attributes longer antisaccade latency to the time it takes to reorient from the visual target to the opposite saccadic target. By elimin

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30

Stufken, John, Angela M. Dean, and Daniel T. Voss. "Design and Analysis of Experiments." Journal of the American Statistical Association 95, no. 450 (2000): 679. http://dx.doi.org/10.2307/2669419.

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31

Durrieu, Gilles, and Laurent Briollais. "Sequential Design for Microarray Experiments." Journal of the American Statistical Association 104, no. 486 (2009): 650–60. http://dx.doi.org/10.1198/jasa.2009.0135.

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32

Iversen, Gudmund R., and Roger G. Petersen. "Design and Analysis of Experiments." Journal of the American Statistical Association 81, no. 396 (1986): 1123. http://dx.doi.org/10.2307/2289104.

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33

Morrey, G. H., S. E. Maxwell, and H. D. Delaney. "Designing Experiments and Analysing Data." Applied Statistics 40, no. 3 (1991): 488. http://dx.doi.org/10.2307/2347533.

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34

Engel, J. "Modelling Variation in Industrial Experiments." Applied Statistics 41, no. 3 (1992): 579. http://dx.doi.org/10.2307/2348091.

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35

Bisgaard, Søren, and Davit Khachatryan. "Quasi-experiments on process dynamics." Journal of the Royal Statistical Society: Series C (Applied Statistics) 60, no. 4 (2011): 497–517. http://dx.doi.org/10.1111/j.1467-9876.2010.00758.x.

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36

Cho, Wendy K. Tam. "Causal inferences from many experiments." Journal of Applied Statistics 44, no. 16 (2016): 2908–22. http://dx.doi.org/10.1080/02664763.2016.1266468.

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37

Lenth, Russell V., and John W. Cotton. "Analyzing within-Subjects Experiments." American Statistician 52, no. 4 (1998): 368. http://dx.doi.org/10.2307/2685446.

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38

Yamamoto, Tetsuo, Hitoshi Miura, and Osama M. Shalabiea. "Thermal desorption induced by chemical reaction on dust surface." Monthly Notices of the Royal Astronomical Society 490, no. 1 (2019): 709–17. http://dx.doi.org/10.1093/mnras/stz2583.

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ABSTRACT We propose a new mechanism of desorption of molecules from dust surface heated by exothermic reactions and derive a formula for the desorption probability. This theory includes no parameter that is physically ambiguous. It can predict the desorption probabilities not only for one-product reactions but also for multiproduct reactions. Furthermore, it can predict desorption probability of a pre-adsorbed molecule induced by a reaction at a nearby site. This characteristic will be helpful to verify the theory by the experiments which involve complex reaction networks. We develop a quantit

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39

Burgess, Leonie, and Deborah J. Street. "Optimal Designs for 2kChoice Experiments." Communications in Statistics - Theory and Methods 32, no. 11 (2003): 2185–206. http://dx.doi.org/10.1081/sta-120024475.

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40

Webb, Steve. "Screening Experiments with Many Factors." Communications in Statistics - Theory and Methods 40, no. 10 (2011): 1879–92. http://dx.doi.org/10.1080/03610921003650432.

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41

Buja, Andreas. "Simultaneously least favorable experiments." Zeitschrift f�r Wahrscheinlichkeitstheorie und Verwandte Gebiete 69, no. 3 (1985): 387–420. http://dx.doi.org/10.1007/bf00532741.

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42

Diecidue, Enrico, Haim Levy, and Moshe Levy. "Probability Dominance." Review of Economics and Statistics 102, no. 5 (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 P

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43

Nelson, Jonathan D., Craig R. M. McKenzie, Garrison W. Cottrell, and Terrence J. Sejnowski. "Experience Matters." Psychological Science 21, no. 7 (2010): 960–69. http://dx.doi.org/10.1177/0956797610372637.

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Deciding which piece of information to acquire or attend to is fundamental to perception, categorization, medical diagnosis, and scientific inference. Four statistical theories of the value of information—information gain, Kullback-Liebler distance, probability gain (error minimization), and impact—are equally consistent with extant data on human information acquisition. Three experiments, designed via computer optimization to be maximally informative, tested which of these theories best describes human information search. Experiment 1, which used natural sampling and experience-based learning

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44

Durlach, Paula J., and Dairn O. Shane. "The Effect of Intertrial Food Presentations on Anticipatory Goal-Tracking in the Rat." Quarterly Journal of Experimental Psychology Section B 46, no. 3b (1993): 289–318. http://dx.doi.org/10.1080/14640749308401090.

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Four experiments examined the sensitivity of anticipatory goal-tracking in the rat to stimulus-food contingency. Contingency was manipulated by varying the probability of food delivery in the absence of a food-tray-light or clicker conditional stimulus (CS), while holding constant the probability of food coincident with the CS. CS control of anticipatory food tray investigation was examined after a period of context extinction in all experiments. Acquisition of stimulus control was undermined by the scheduling of intertrial food deliveries (Experiment 1). The rate of intertrial food deliveries

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45

McNicol, J. W., and R. G. Petersen. "Agricultural Field Experiments." Journal of the Royal Statistical Society. Series A (Statistics in Society) 159, no. 1 (1996): 190. http://dx.doi.org/10.2307/2983493.

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46

Birnbaum, Michael H., and Sandra V. Wakcher. "Web-based experiments controlled by JavaScript: An example from probability learning." Behavior Research Methods, Instruments, & Computers 34, no. 2 (2002): 189–99. http://dx.doi.org/10.3758/bf03195442.

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47

TODA, Kyoko, Seiichi ISHIDA, Kotoko NAKATA, et al. "Test of Significant Differences with a priori Probability in Microarray Experiments." Analytical Sciences 19, no. 11 (2003): 1529–35. http://dx.doi.org/10.2116/analsci.19.1529.

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48

Damgaard, Christian. "PLANT COMPETITION EXPERIMENTS: TESTING HYPOTHESES AND ESTIMATING THE PROBABILITY OF COEXISTENCE." Ecology 79, no. 5 (1998): 1760–67. http://dx.doi.org/10.1890/0012-9658(1998)079[1760:pcetha]2.0.co;2.

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49

Fischer, R., M. Mayer, W. von der Linden, and V. Dose. "Energy resolution enhancement in ion beam experiments with Bayesian probability theory." Nuclear Instruments and Methods in Physics Research Section B: Beam Interactions with Materials and Atoms 136-138 (March 1998): 1140–45. http://dx.doi.org/10.1016/s0168-583x(97)00907-5.

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50

Arritt, Raymond W., and William M. Frank. "Experiments in probability of Precipitation Amount Forecasting Using Model Output Statistics." Monthly Weather Review 113, no. 11 (1985): 1837–51. http://dx.doi.org/10.1175/1520-0493(1985)113<1837:eipopa>2.0.co;2.

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