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Books on the topic 'Win probability'

Author: Grafiati
Published: 4 June 2021
Last updated: 19 June 2025

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1

Hans, Riedwyl, ed. How to win more: Strategies for increasing a lottery win. A K Peters, 1998.

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2

M, Shiao, Chamis C. C, and United States. National Aeronautics and Space Administration., eds. Probabilistic assessment of National Wind Tunnel. National Aeronautics and Space Administration, 1996.

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3

John F. Kennedy Space Center. and United States. National Aeronautics and Space Administration. Scientific and Technical Information Program., eds. Statistical short-range guidance for peak wind speed forecasts on Kennedy Space Center/Cape Canaveral Air Force Station: Phase 1 results. National Aeronautics and Space Administration, Office of Management, Scientific and Technical Information Program, 2002.

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4

John F. Kennedy Space Center. and United States. National Aeronautics and Space Administration. Scientific and Technical Information Program., eds. Statistical short-range guidance for peak wind speed forecasts on Kennedy Space Center/Cape Canaveral Air Force Station: Phase 1 results. National Aeronautics and Space Administration, Office of Management, Scientific and Technical Information Program, 2002.

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5

United States. Army Aviation Research and Technology Activity. and Langley Research Center, eds. A comparison of fatigue life prediction methodologies for rotor craft. National Aeronautics and Space Administration, Langley Research Center, 1990.

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6

Pierson, Willard J. Probabilities and statistics for backscatter estimates obtained by a scatterometer with applications to new scatterometer design data. National Aeronautics and Space Administration, Office of Management, Scientific and Technical Information Division, 1989.

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7

Gdala, Amy. Heads We Win (Probability Sequence). Superscript, 2007.

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8

Ratford, Scotty. High Probability Day Trading Strategies: High Win Rate Day Trading and Scalping Strategies for Trading Crypto, Forex and Stocks. Independently Published, 2022.

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9

Probability theory: The logic of science = Gai lü lun chen si lu (Ying wen ban). Ren min you dian chu ban she, 2009.

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10

Montgomery, Douglas C. Applied Statistics and Probability for Engineers Wie. Wiley & Sons, Incorporated, John, 2002.

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11

Montgomery, Douglas C. WIE ASE Applied Statistics and Probability for Engineers. John Wiley & Sons Inc, 2006.

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12

Dawid, A. Philip, Julia Mortera, and Paola Vicard. Volatility in prediction markets: A measure of information flow in political campaigns. Edited by Anthony O'Hagan and Mike West. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780198703174.013.21.

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This article discusses the use of Bayesian analysis in the evaluation of temporal volatility and information flows in political campaigns. Using the 2004 US presidential election campaign as a case study, it demonstrates the utility of a model with two volatility regimes that simplifies the task of associating events with periods of high information. The article first explains why prediction markets are able to aggregate information such that the prices of future contracts are reflective of the event’s actual probability of occurring before analysing data from futures on ‘Bush wins the popular vote in 2004’, or the traded probability, of Bush winning the election. These data are used to build a measure of information flow. The results show that information flows increased as a result of the televised debates, and that these debates, along with the selection of the vice presidential candidate, increased prediction market volatility.
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13

Bao, Yun, Carl Chiarella, and Boda Kang. Particle Filters for Markov-Switching Stochastic Volatility Models. Edited by Shu-Heng Chen, Mak Kaboudan, and Ye-Rong Du. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780199844371.013.9.

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This chapter proposes an auxiliary particle filter algorithm for inference in regime switching stochastic volatility models in which the regime state is governed by a first-order Markov chain. It proposes an ongoing updated Dirichlet distribution to estimate the transition probabilities of the Markov chain in the auxiliary particle filter. A simulation-based algorithm is presented for the method that demonstrates the ability to estimate a class of models in which the probability that the system state transits from one regime to a different regime is relatively high. The methodology is implemented in order to analyze a real-time series, namely, the foreign exchange rate between the Australian dollar and the South Korean won.
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14

Crupi, Vincenzo, and Katya Tentori. Confirmation Theory. Edited by Alan Hájek and Christopher Hitchcock. Oxford University Press, 2017. http://dx.doi.org/10.1093/oxfordhb/9780199607617.013.33.

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We first discuss several qualitative properties of confirmation as analyzed in a probabilistic framework. Some of these properties are classical, while others are relatively novel; some are shared by absolute and incremental confirmation, others are distinctive for each kind. We then proceed to address axiomatic characterizations of major classes of probabilistic measures of incremental confirmation. This treatment includes an original result displaying how conditions which single out the traditional probability difference measure up to ordinal equivalence. Finally, we argue that the longstanding project of a compelling confirmation-theoretic generalization of logical entailment (and refutation) can be achieved, provided that the right explicatum is adopted (to wit, a relative distance measure). This conclusion, we submit, dispels concerns that have been aired in the literature up to recent times.
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15

Statistical short-range guidance for peak wind speed forecasts on Kennedy Space Center/Cape Canaveral Air Force Station: Phase 1 results. National Aeronautics and Space Administration, Office of Management, Scientific and Technical Information Program, 2002.

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16

Regression Modelling Wih Spatial and Spatial-Temporal Data: A Bayesian Approach. Taylor & Francis Group, 2020.

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17

Li, Guangquan, and Robert P. Haining. Regression Modelling Wih Spatial and Spatial-Temporal Data: A Bayesian Approach. Taylor & Francis Group, 2020.

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18

Rautenstrauch, Johann. Jesuitengift Wie Es Unter Clemes XIII. Entdeckt, Unter Clemens XIV. Unterdrückt, und Unter Pius VI. Noch Fortschleicht, Oder der Jesuit in Fünferlei Gestalten, Allen Christen Zur Warnung Vorgestellt, Als Probabilist, Beichtvater, Retzermacher, Fürst... Creative Media Partners, LLC, 2018.

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19

Rautenstrauch, Johann. Jesuitengift Wie Es Unter Clemes XIII. Entdeckt, Unter Clemens XIV. Unterdrückt, und Unter Pius VI. Noch Fortschleicht, Oder der Jesuit in Fünferlei Gestalten, Allen Christen Zur Warnung Vorgestellt, Als Probabilist, Beichtvater, Retzermacher, Fürst... Creative Media Partners, LLC, 2018.

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