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Books on the topic 'OR with probabilistic transitions'

Author: Grafiati

Published: 4 June 2021

Last updated: 13 February 2022

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1

Haenni, Rolf, Jan-Willem Romeijn, Gregory Wheeler, and Jon Williamson. Probabilistic Logics and Probabilistic Networks. Springer Netherlands, 2011. http://dx.doi.org/10.1007/978-94-007-0008-6.

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2

Probabilistic logics and probabilistic networks. Springer, 2011.

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3

Probabilistic databases. Morgan & Claypool, 2011.

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4

Mitrani, I. Probabilistic modelling. Cambridge University Press, 1998.

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5

name, No. Probabilistic linguistics. MIT Press, 2003.

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6

Eells, Ellery. Probabilistic causality. Cambridge University Press, 1991.

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7

Probabilistic inequalities. World Scientific, 2010.

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8

Thrun, Sebastian. Probabilistic robotics. MIT Press, 2005.

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9

Chernoff, Egan J., and Bharath Sriraman, eds. Probabilistic Thinking. Springer Netherlands, 2014. http://dx.doi.org/10.1007/978-94-007-7155-0.

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10

Paass, Gerhard. Probabilistic logic. Gesellschaft fur Mathematik und Datenverarbeitung, 1987.

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11

Wolfram, Burgard, and Fox Dieter, eds. Probabilistic robotics. MIT Press, 2005.

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12

Lee, Won Don. Probabilistic inference. Dept. of Computer Science, University of Illinois at Urbana-Champaign, 1986.

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13

Cameron, Julia. Transitions. Penguin USA, Inc., 2009.

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14

Transitions. DeVorss Publications, 1995.

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15

Gaboury-Diallo, Lise. Transitions. Éditions du Blé, 2002.

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16

Ibnijah. Transitions. Creative Publishers, 1990.

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17

Poppe, Krijn J., Catherine Termeer, and Maja Slingerland, eds. Transitions. Wageningen Academic Publishers, 2009. http://dx.doi.org/10.3920/978-90-8686-688-5.

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18

Gnedenko, B. V. Probabilistic reliability engineering. Wiley, 1995.

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19

Hynes, Mary Ellen. Probabilistic liquefaction analysis. Division of Engineering Technology, Office of Nuclear Regulatory Research, U.S. Nuclear Regulatory Commission, 1999.

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20

Alon, Noga. The probabilistic method. 3rd ed. John Wiley, 2008.

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21

Alon, Noga. The probabilistic method. 3rd ed. John Wiley, 2008.

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22

Alon, Noga. The probabilistic method. 2nd ed. Wiley, 2000.

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23

Hynes, M. E. Probabilistic liquefaction analysis. U.S. Nuclear Regulatory Commission, 1990.

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24

Alon, Noga. The probabilistic method. Wiley, 1992.

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25

Fong, Rebecca Pui Shan. Probabilistic fault diagnosis. National Library of Canada, 2003.

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26

Schweizer, B. Probabilistic metric spaces. Dover Publications, 2005.

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27

Shafer, Glenn. Probabilistic expert systems. Society for Industrial and Applied Mathematics, 1996.

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28

Ushakov, I. A. Probabilistic reliability models. Wiley, 2012.

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29

H, Spencer Joel, ed. The probabilistic method. John Wiley & Sons, Inc., 2016.

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30

Tammen, Ronald L., Jacek Kugler, and Douglas Lemke. Foundations of Power Transition Theory. Oxford University Press, 2017. http://dx.doi.org/10.1093/acrefore/9780190228637.013.296.

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Power Transition theory is a dynamic and structural model for analyzing fundamental shifts in global power. The theory itself, while maintaining its core concepts, has metamorphosed over time by adding new dimensions and addressing new topics. It is both data based and qualitatively intuitive.As a probabilistic theory, it has proven useful in predicting the conditions that forecast both conflict and cooperation at the global, national, and subnational levels of analysis. As a foreign policy tool, it creates historical signposts pointing toward tectonic shifts in nation state and alliance power profiles.

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31

Williamson, Jon, Rolf Haenni, Gregory Wheeler, and Jan-Willem Romeijn. Probabilistic Logics and Probabilistic Networks. Springer, 2013.

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32

Haenni, Rolf, Gregory Wheeler, and Jan-Willem Romeijn. Probabilistic Logics and Probabilistic Networks. Springer, 2011.

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33

1945-, Gelenbe E., and Mitrani I, eds. Probabilistic modelling. North-Holland, 1994.

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34

Probabilistic Metaphysics. Blackwell Pub, 1986.

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35

Spencer, Joel H., and Noga Alon. Probabilistic Method. Wiley & Sons, Incorporated, John, 2008.

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36

Spencer, Joel H., and Noga Alon. Probabilistic Method. Wiley & Sons, Incorporated, John, 2015.

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37

Spencer, Joel H., and Noga Alon. Probabilistic Method. Wiley & Sons, Incorporated, John, 2015.

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38

Probabilistic Knowledge. Oxford University Press, 2018.

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39

Burgard, Wolfram, Dieter Fox, Sebastian Thrun, and Ronald C. Arkin. Probabilistic Robotics. MIT Press, 2005.

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40

Spencer, Joel H., and Noga Alon. Probabilistic Method. Wiley & Sons, Incorporated, John, 2011.

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41

Burgard, Wolfram, Dieter Fox, and Sebastian Thrun. Probabilistic Robotics. MIT Press, 2005.

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42

Spencer, Joel H., and Noga Alon. Probabilistic Method. Wiley & Sons, Incorporated, John, 2008.

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43

Hay, Jennifer, Rens Bod, and Stefanie Jannedy. Probabilistic Linguistics. MIT Press, 2003.

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44

Hay, Jennifer, Rens Bod, and Stefanie Jannedy. Probabilistic Linguistics. MIT Press, 2003.

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45

1965-, Bod Rens, Hay Jennifer, and Jannedy Stefanie, eds. Probabilistic linguistics. MIT Press, 2003.

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46

Bod, Rens, Jennifer Hay, and Stefanie Jannedy, eds. Probabilistic Linguistics. The MIT Press, 2003. http://dx.doi.org/10.7551/mitpress/5582.001.0001.

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47

Bod, Rens. Probabilistic Linguistics. Oxford University Press, 2009. http://dx.doi.org/10.1093/oxfordhb/9780199544004.013.0025.

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48

Williamson, Jon. Probabilistic Theories. Edited by Helen Beebee, Christopher Hitchcock, and Peter Menzies. Oxford University Press, 2010. http://dx.doi.org/10.1093/oxfordhb/9780199279739.003.0010.

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Causal relationships are typically accompanied by probabilistic dependencies — normally when A causes B the former raises or lowers the probability of the latter. Probabilistic theories of causality usually try to characterize or analyse causality in terms of these probabilistic dependencies: they try to provide probabilistic criteria for deciding whether A causes B, and often maintain that causality just is the corresponding pattern of probabilistic relationships. This article provides an introduction to and criticism of such accounts. While it is argued that probabilistic theories are ultimately unsuccessful, work on probabilistic causality has shed a great deal of light on the relationship between causality and probability and hence these theories repay a thorough understanding.

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49

Hitchcock, Christopher. Probabilistic Causation. Edited by Alan Hájek and Christopher Hitchcock. Oxford University Press, 2017. http://dx.doi.org/10.1093/oxfordhb/9780199607617.013.41.

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This chapter will explore a variety of projects that aim to characterize causal concepts using probability. These are, somewhat arbitrarily, divided into four categories. First, a tradition within philosophy that has aimed to define, or at least constrain, causation in terms of conditional probability is discussed. Secondly, the use of causal Bayes nets to represent causal relations, to facilitate inferences from probabilities to causal relations, and to ‘identify’ causal quantities in probabilistic terms is discussed. Thirdly, efforts to measure causal strength in probabilistic terms are reviewed, with particular attention to the significance of these measures in the context of epidemiology. Finally, attempts are discussed to analyze the relation of ‘actual causation’ (sometimes called ‘singular causation’) using probability.

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50

Eells, Ellery. Probabilistic Causality. Cambridge University Press, 2008.

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