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Uncertain Inference
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  • Cited by 35
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    This book has been cited by the following publications. This list is generated based on data provided by CrossRef.

    Kyburg, H.E. 2001. Statistical considerations in learning from data. p. 321.

    Kyburg Jr., Henry E. 2006. Belief, Evidence, and Conditioning*. Philosophy of Science, Vol. 73, Issue. 1, p. 42.

    Zenker, Frank 2006. Monotonicity and Reasoning with Exceptions. Argumentation, Vol. 20, Issue. 2, p. 227.

    Kyburg, Jr., Henry E. and Teng, Choh Man 2006. NONMONOTONIC LOGIC AND STATISTICAL INFERENCE*. Computational Intelligence, Vol. 22, Issue. 1, p. 26.

    Dietz, Richard 2008. BETTING ON BORDERLINE CASES1. Philosophical Perspectives, Vol. 22, Issue. 1, p. 47.

    Douven, Igor 2008. Knowledge and Practical Reasoning. Dialectica, Vol. 62, Issue. 1, p. 101.

    Walker, Vern R. 2009. Handbook of Research on Agent-Based Societies. p. 305.

    Levi, Isaac 2010. Probability logic, logical probability, and inductive support. Synthese, Vol. 172, Issue. 1, p. 97.

    Brandolini, Silva Marzetti Dall’Aste and Scazzieri, Roberto 2011. Fundamental Uncertainty. p. 1.

    Walker, Vern R. Carie, Nathaniel DeWitt, Courtney C. and Lesh, Eric 2011. A framework for the extraction and modeling of fact-finding reasoning from legal decisions: lessons from the Vaccine/Injury Project Corpus. Artificial Intelligence and Law, Vol. 19, Issue. 4, p. 291.

    Bickel, David R. 2011. Estimating the Null Distribution to Adjust Observed Confidence Levels for Genome-Scale Screening. Biometrics, Vol. 67, Issue. 2, p. 363.

    Kyburg, Henry E. 2011. Fundamental Uncertainty. p. 23.

    Teng, Choh Man 2012. When adjunction fails. Synthese, Vol. 186, Issue. 2, p. 501.

    Thorn, Paul D. 2012. Two Problems of Direct Inference. Erkenntnis, Vol. 76, Issue. 3, p. 299.

    Ilić-Stepić, Angelina Ognjanović, Zoran Ikodinović, Nebojša and Perović, Aleksandar 2012. A p-adic probability logic. Mathematical Logic Quarterly, Vol. 58, Issue. 4-5, p. 263.

    Howson, Colin 2012. Modelling uncertain inference. Synthese, Vol. 186, Issue. 2, p. 475.

    Bickel, David R. 2012. A frequentist framework of inductive reasoning. Sankhya A, Vol. 74, Issue. 2, p. 141.

    Kovalerchuk, Boris Perlovsky, Leonid and Wheeler, Gregory 2012. Modelling phenomena and dynamic logic of phenomena. Journal of Applied Non-Classical Logics, Vol. 22, Issue. 1-2, p. 53.

    Osimani, Barbara 2013. The precautionary principle in the pharmaceutical domain: a philosophical enquiry into probabilistic reasoning and risk aversion. Health, Risk & Society, Vol. 15, Issue. 2, p. 123.

    Krzyżanowska, K. Wenmackers, S. and Douven, I. 2013. Inferential Conditionals and Evidentiality. Journal of Logic, Language and Information, Vol. 22, Issue. 3, p. 315.

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    Uncertain Inference
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Book description

Coping with uncertainty is a necessary part of ordinary life and is crucial to an understanding of how the mind works. For example, it is a vital element in developing artificial intelligence that will not be undermined by its own rigidities. There have been many approaches to the problem of uncertain inference, ranging from probability to inductive logic to nonmonotonic logic. Thisbook seeks to provide a clear exposition of these approaches within a unified framework. The principal market for the book will be students and professionals in philosophy, computer science, and AI. Among the special features of the book are a chapter on evidential probability, which has not received a basic exposition before; chapters on nonmonotonic reasoning and theory replacement, matters rarely addressed in standard philosophical texts; and chapters on Mill's methods and statistical inference that cover material sorely lacking in the usual treatments of AI and computer science.


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