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Книги з теми "Prediction Explanation"

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Автор: Grafiati

Опубліковано: 14 березня 2023

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1

Dieks, Dennis, Wenceslao J. Gonzalez, Stephan Hartmann, Thomas Uebel, and Marcel Weber, eds. Explanation, Prediction, and Confirmation. Dordrecht: Springer Netherlands, 2011. http://dx.doi.org/10.1007/978-94-007-1180-8.

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2

Dennis Geert Bernardus Johan Dieks. Explanation, Prediction, and Confirmation. Dordrecht: Springer Science+Business Media B.V., 2011.

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3

P, Farrington David, ed. Criminal recidivism: Explanation, prediction and prevention. Abingdon, Oxon: Routledge, 2015.

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4

Bonneson, James, and John Ivan. Theory, Explanation, and Prediction in Road Safety. Washington, D.C.: Transportation Research Board, 2013. http://dx.doi.org/10.17226/22465.

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5

Multiple regression in behavioral research: Explanation and prediction. 3rd ed. Forth Worth: Harcourt Brace College Publishers, 1997.

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6

L, Casti J., Karlqvist Anders, and Sweden Forskningsrådsnämnden, eds. Beyond belief: Randomness, prediction, and explanation in science. Boca Raton, Fla: CRC Press, 1991.

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7

Eriksson, Bo G. Studying ageing: Experiences, description, variation, prediction and explanation. Göteborg: Department of Sociology, University of Gothenburg, 2010.

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8

Eriksson, Bo G., and Bo G. Eriksson. Studying ageing: Experiences, description, variation, prediction and explanation. Göteborg: Department of Sociology, University of Gothenburg, 2010.

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9

Trasler, Gordon. The Explanation of criminality. London: Routledge & Kegan Paul, 1998.

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10

Rastogi, P. N. Ethnic tensions in Indian society: Explanation, prediction, monitoring, and control. Delhi, India: Mittal Publications, 1986.

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11

Waskan, Jonathan A. Models and cognition: Prediction and explanation in everyday life and in science. Cambridge, Mass: MIT Press, 2006.

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12

Kenkyūjo, Kokuritsu Kankyō, ed. Ozon-sō hakai no chōki hendō yōin no kaiseki to shōrai yosoku ni kansuru kenkyū: Heisei 14-nendo--Heisei 18-nendo = Research on explanation of long-term trend and prediction of future change of ozone layer. [Tsukuba-shi: Kokuritsu Kankyō Kenkyūjo, 2007.

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13

Kenkyūjo, Kokuritsu Kankyō, ed. Ozon-sō hakai no chōki hendō yōin no kaiseki to shōrai yosoku ni kansuru kenkyū: Heisei 14-nendo--Heisei 18-nendo = Research on explanation of long-term trend and prediction of future change of ozone layer. [Tsukuba-shi: Kokuritsu Kankyō Kenkyūjo, 2007.

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14

Olmsted, Garrett S. The Gaulish Calendar: A Reconstruction From the Bronze Fragments From Coligny, With An Analysis of its Function as a Highly Accurate Lunar-Solar Predictor, as Well as an Explanation of its Terminology and Development. Bonn: R. Habelt, 1992.

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15

Hartmann, Stephan, Dennis Dieks, and Wenceslao J. Gonzalez. Explanation, Prediction, and Confirmation. Springer, 2011.

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16

Weber, Marcel, Stephan Hartmann, Dennis Dieks, Thomas Uebel, and Wenceslao J. Gonzalez. Explanation, Prediction, and Confirmation. Dennis Dieks, 2013.

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17

Lissack, M., and A. Graber. Modes of Explanation: Affordances for Action and Prediction. Palgrave Macmillan, 2014.

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18

Modes of Explanation: Affordances for Action and Prediction. Palgrave Macmillan, 2014.

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19

Lissack, M., and A. Graber. Modes of Explanation: Affordances for Action and Prediction. Palgrave Macmillan, 2014.

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20

Casti, J. L. Beyond Belief: Randomness, Prediction, and Explanation in Science. CRC Press, 1991.

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21

Casti, J. L. Beyond Belief: Randomness, Prediction and Explanation in Science. Taylor & Francis Group, 2018.

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22

Beyond Belief: Randomness, Prediction and Explanation in Science. Taylor & Francis Group, 2018.

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23

Casti, J. L. Beyond Belief: Randomness, Prediction and Explanation in Science. Taylor & Francis Group, 2018.

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24

Casti, J. L. Beyond Belief: Randomness, Prediction and Explanation in Science. Taylor & Francis Group, 2018.

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25

Beyond Belief: Randomness, Prediction and Explanation in Science. Taylor & Francis Group, 2018.

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26

Webber, Michael J. Explanation, Prediction and Planning (Research in Planning & Design). Pion, 1989.

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27

Martino, Alessio, and Alessandro Giuliani, eds. Prediction and Explanation in Biomedicine using Network-Based Approaches. Frontiers Media SA, 2022. http://dx.doi.org/10.3389/978-2-83250-222-8.

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28

Clements, Philip J. The Next 25 Years: An Explanation Not a Prediction. Center For Christian Business Ethics Today, LLC., 2013.

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29

Anjum, Rani Lill, and Stephen Mumford. Risky Predictions. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198733669.003.0022.

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Анотація:

Prediction is often seen as explanation’s poor relation. Yet prediction can be more useful than explanation because it is often future-directed and a reason for action. Predictions can still be useful even with a degree of fallibility, which is important since there is no necessity in nature upon which infallible prediction could rest. Indeed, it is an evident weakness of any theory of prediction if it cannot account for the possibility that the very best theory, given the evidence, could still be false.

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30

Birch, Jonathan. The Rule under Attack. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780198733058.003.0003.

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Анотація:

HRG has been criticized for being an ‘empty statement’ or tautology, for failing to yield predictions, and for failing to yield causal explanations of change. There is some justification for these charges, yet they do not undermine the value of HRG as an organizing framework. In response to the ‘tautology’ complaint, we should admit that HRG is tautology-like, in that it avoids detailed dynamical assumptions. But this is an advantage in an organizing framework, because it ensures its compatibility with a wide range of more detailed models. In response to the ‘prediction’ complaint, we should concede that HRG is not very useful for prediction, but the role of an organizing framework is not predictive. In response to the ‘causal explanation’ complaint, this chapter argues that HRG, by organizing our thinking about ultimate causes, generates understanding of those causes. It also compares favourably to other possible organizing frameworks.

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31

Kosta, Peter, and Katrin Schlund, eds. Keynotes from the International Conference on Explanation and Prediction in Linguistics (CEP): Formalist and Functionalist Approaches. Peter Lang D, 2021. http://dx.doi.org/10.3726/b18569.

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32

Kosta, Peter, ed. Keynotes from the International Conference on Explanation and Prediction in Linguistics (CEP): Formalist and Functionalist Approaches. Peter Lang D, 2021. http://dx.doi.org/10.3726/9783631858202.003.0005.

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33

Kosta, Peter, ed. Keynotes from the International Conference on Explanation and Prediction in Linguistics (CEP): Formalist and Functionalist Approaches. Peter Lang D, 2021. http://dx.doi.org/10.3726/9783631858202.003.0004.

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34

Kosta, Peter, ed. Keynotes from the International Conference on Explanation and Prediction in Linguistics (CEP): Formalist and Functionalist Approaches. Peter Lang D, 2021. http://dx.doi.org/10.3726/9783631858202.003.0002.

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35

Kosta, Peter, ed. Keynotes from the International Conference on Explanation and Prediction in Linguistics (CEP): Formalist and Functionalist Approaches. Peter Lang D, 2021. http://dx.doi.org/10.3726/9783631858202.003.0003.

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36

Kosta, Peter, ed. Keynotes from the International Conference on Explanation and Prediction in Linguistics (CEP): Formalist and Functionalist Approaches. Peter Lang D, 2021. http://dx.doi.org/10.3726/9783631858202.003.0001.

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37

Kosta, Peter, and Katrin Schlund. Keynotes from the International Conference on Explanation and Prediction in Linguistics : Formalist and Functionalist Approaches: Heidelberg, February 13th And 14th 2019. Lang GmbH, Internationaler Verlag der Wissenschaften, Peter, 2021.

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38

Kosta, Peter, and Katrin Schlund. Keynotes from the International Conference on Explanation and Prediction in Linguistics : Formalist and Functionalist Approaches: Heidelberg, February 13th And 14th 2019. Lang GmbH, Internationaler Verlag der Wissenschaften, Peter, 2021.

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39

Kosta, Peter, and Katrin Schlund. Keynotes from the International Conference on Explanation and Prediction in Linguistics : Formalist and Functionalist Approaches: Heidelberg, February 13th And 14th 2019. Lang GmbH, Internationaler Verlag der Wissenschaften, Peter, 2021.

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40

Anjum, Rani Lill, and Stephen Mumford. Does Science Need Laws of Nature? Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198733669.003.0018.

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Анотація:

There is a view that as well as causation, science invokes general laws of nature. In one account, the universe is law-governed, but it is not clear whether this is to be understood literally or not. The generality of laws is useful in prediction and explanation. Laws provide also systematicity and simplicity. But it is questionable what the real being of laws is. Their being might reduce to singular powers or tendencies of individual things. While laws could be used in prediction and explanation, it is not clear that laws are indispensable for this purpose. Singular powers might be able to play the same role: or play it better.

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41

Gerstenberg, Tobias, and Joshua B. Tenenbaum. Intuitive Theories. Edited by Michael R. Waldmann. Oxford University Press, 2017. http://dx.doi.org/10.1093/oxfordhb/9780199399550.013.28.

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Анотація:

This chapter first explains what intuitive theories are, how they can be modeled as probabilistic, generative programs, and how intuitive theories support various cognitive functions such as prediction, counterfactual reasoning, and explanation. It focuses on two domains of knowledge: people’s intuitive understanding of physics, and their intuitive understanding of psychology. It shows how causal judgments can be modeled as counterfactual contrasts operating over an intuitive theory of physics, and how explanations of an agent’s behavior are grounded in a rational planning model that is inverted to infer the agent’s beliefs, desires, and abilities. It concludes by highlighting some of the challenges that the intuitive theories framework faces, such as understanding how intuitive theories are learned and developed.

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42

Ray, Sumantra (Shumone), Sue Fitzpatrick, Rajna Golubic, Susan Fisher, and Sarah Gibbings, eds. Navigating research methods: basic concepts in biostatistics and epidemiology. Oxford University Press, 2016. http://dx.doi.org/10.1093/med/9780199608478.003.0002.

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Анотація:

This chapter provides an overview of the basic concepts in biostatistics and epidemiology. Section 1: Basic concepts in biostatistics The concepts in biostatistics include: 1. descriptive statistical methods (which comprise of frequency distribution, distribution shapes, and measures of central tendency and dispersion); and 2. inferential statistics which is applied to make inferences about a population from the sample data. Non-probability and probability sampling methods are outlined. This section provides simple explanation of the complex concepts of significance tests and confidence intervals and their corresponding interpretation. Correlation and regression methods used to describe the association between two quantitative variables are also explained. This section also provides an overview of when to use which statistical test given the type of data and the nature of the research question. Section 2: Basic concepts in epidemiology This section begins with the definitions of normality. Next, the interpretation of diagnostic tests and clinical prediction are explained and the definitions of sensitivity, specificity, positive predictive value and negative predictive value are provided. The relationship between these four constructs is discussed. The application of this concepts in the treatment and prevention is discussed.

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43

Azzopardi, Leif, and Guido Zuccon. Economic Models of Interaction. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198799603.003.0012.

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Анотація:

This chapter provides a tutorial on how economics can be used to model the interaction between users and systems. Economic theory provides an intuitive and natural way to model Human-Computer Interaction which enables the prediction and explanation of user behaviour. A central tenet of the approach is the utility maximisation paradigm where it is assumed that users seek to maximise their profit/benefit subject to budget and other constraints when interacting with a system. By using such models it is possible to reason about user behaviour and make predictions about how changes to the interface or the users interactions will affect performance and behaviour. In this chapter, we describe and develop several economic models relating to how users search for information. While the examples are specific to Information Seeking and Retrieval, the techniques employed can be applied more generally to other human-computer interaction scenarios. Therefore, the goal of this chapter is to provide an introduction and overview of how to build economic models of human-computer interaction that generate testable hypotheses regarding user behaviour which can be used to guide design and inform experimentation.

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44

Anjum, Rani Lill, and Stephen Mumford. Is the Business of Science to Construct Theories? Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198733669.003.0011.

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Анотація:

Science is concerned with empirical knowledge, and because of this there is some reluctance to construct theories as they go beyond the observed evidence. But there is only so much that the data can do on their own. For explanation and prediction, for instance, we need a theory in addition, and the difficulty here is that no amount of data will completely dictate the truth of such a theory. In any case, there are some grounds to believe that theory is needed in order to generate the data in the first place. We cannot start with a mythical ‘raw data’.

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45

Kohn, Robert, Jessica Warner, Wendy Verhoek-Oftedahl, and Emily Murphy. Elder Abuse. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780199374656.003.0021.

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Анотація:

There are five principal domains of elder abuse: physical abuse, psychological abuse, sexual abuse, neglect, and financial abuse. This chapter discusses the prevalence, prediction, and assessment of elder abuse as well as other factors related to abuse. The incidence of elder abuse is 24 times greater than the number of cases referred to social service, law enforcement, or other legal authorities. Caregiver factors, rather than risk factors associated with the abused elder, may be more important in predicting abuse. Lack of compliance with medical regimens, delay in seeking medical care, disparity in explanations given for injuries, unexplained injuries, and implausible or vague explanations may be warning signs. Elder financial victimization can be classified as door-to-door scams, professional swindles, and caregiver abuse. Elder abuse increases mortality, emergency room visits, hospitalizations, and skilled nursing home placement. The psychiatric assessment may raise red flags of suspected elder abuse and may necessitate reporting to adult protective services. Interventions should be tailored to the circumstances and the resources available.

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46

Predictions, Explanations and Causal Effects from Longitudinal Data (Professorial Lectures). Institute of Education, 2007.

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47

Healey, Richard. Interlude: Some Alternative Interpretations. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780198714057.003.0007.

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An understanding of quantum theory is manifested by the ability successfully and unproblematically to use it to further the scientific goals of prediction, explanation, and control of natural phenomena. An Interpretation seeks further to formulate or reformulate it as a fundamental theory that provides a self-contained description of the world. I critically review three prominent but radically different Interpretations of quantum theory (Bohmian mechanics, non-linear theories, Everettian quantum mechanics) and give my reasons for rejecting each as a way of understanding quantum theory. These include problems associated with non-locality, failure of relativistic invariance, empirical inaccessibility, and decision-making. We can achieve a satisfactory understanding of quantum theory and how it successfully advances the goals of science without providing an Interpretation of the theory.

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48

Ray, Sumantra (Shumone), Sue Fitzpatrick, Rajna Golubic, Susan Fisher, and Sarah Gibbings, eds. Research: why and how? Oxford University Press, 2016. http://dx.doi.org/10.1093/med/9780199608478.003.0001.

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Анотація:

This chapter sets the scene of the book and provides a rationale for why research should be done. It begins with a definition of research and continues with a brief historical overview of medical research. The pivotal role of research in everyday medical practice is explained. The key databases containing published peer-reviewed articles related to clinical medicine and healthcare are detailed. The three major goals of research (description, explanation and prediction) are outlined. This chapter further focuses on the principles of the scientific method, its characteristics and main steps. Several classifications of the types of research are also presented. Bradford-Hill's proposed criteria for causality are also outlined. The hierarchy of research evidence and the corresponding levels of recommendations are also presented.

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49

Guala, Francesco. Philosophy of the Social Sciences. Edited by Paul Humphreys. Oxford University Press, 2015. http://dx.doi.org/10.1093/oxfordhb/9780199368815.013.6.

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Анотація:

Naturalism is still facing a strong opposition in the philosophy of social science from influential scholars who argue that philosophical analysis must be autonomous from scientific investigation. The opposition exploits philosophers’ traditional diffidence toward social science and fuels the ambition to provide new foundations for social research. A classic anti-naturalist strategy is to identify a feature of social reality that prevents scientific explanation and prediction. An all-time favorite is the dependence of social phenomena on human representation. This article examines two prominent versions of the dependence thesis and concludes that they both fail. Contemporary social science is capable of accounting for the causal dependence of social reality on representation, and there is no reason to believe that social entities are ontologically dependent on the collective acceptance of a constitutive rule.

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50

Love, Alan C. Individuation, Individuality, and Experimental Practice in Developmental Biology. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780190636814.003.0008.

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Philosophical analyses of individuals in biology have focused on theories of individuality that derive from evolutionary theory, understood as a fundamental framework that governs all of biology. Less attention has been given to how individuals are determined in experimental practice. The author uses the problem agenda of growth in developmental biology to argue that individuation practices are guided by structured problems, which leads to variable and divergent conceptualizations of what qualifies as an individual. The result is a pluralist perspective on biological individuality in which different kinds of individuals are counted, compared, detected, or tracked in practice. The problem-relative nature of individuation dissolves the so-called problem of biological individuality and captures more accurately how biologists engage in successful practices that contribute to the manipulation, prediction, and explanation of biological individuals.

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