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Books on the topic 'Induction and inference'

Author: Grafiati

Published: 4 June 2021

Last updated: 13 February 2022

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1

Fred, Wilson. Hume's defence of causal inference. University of Toronto Press, 1997.

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2

Moore, Kathleen Dean. Inductive arguments: A field guide. Kendall/Hunt, 1986.

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3

Rodríguez, Andrés Rivadulla. Probabilidad e inferencia científica. Anthropos, 1991.

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4

Realismus von subjektiven Wahrscheinlichkeiten: Eine kognitionspsychologische Analyse inferentieller Prozesse beim Overconfidence-Phänomen. P. Lang, 1987.

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5

Truth strategy simplified. Thales, 1999.

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6

Levi, Isaac. For the sake of the argument: Ramsey Test conditionals, inductive inference, and nonmonotonic reasoning. Cambridge University Press, 1996.

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7

Kornblith, Hilary. Inductive inference and its natural ground: An essay in naturalistic epistemology. MIT Press, 1993.

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8

Zabell, S. L. Symmetry and its discontents: Essays on the history of inductive probability. Cambridge University Press, 2006.

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9

Thomas, Dewing R., and Perini Matthew J. 1973-, eds. Inference: Teaching students to develop hypotheses, evaluate evidence, and draw logical conclusions : a strategic teacher PLC guide. ASCD, 2012.

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10

Zilles, Sandra. Uniform learning of recursive functions. Akademische Verlagsgesellschaft Aka, 2003.

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11

Caianiello, Pasquale. Inductive inference and encoding. Courant Institute of Mathematical Sciences, New York University, 1988.

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12

Jantke, Klaus P., ed. Analogical and Inductive Inference. Springer Berlin Heidelberg, 1992. http://dx.doi.org/10.1007/3-540-56004-1.

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13

Jantke, Klaus P., ed. Analogical and Inductive Inference. Springer Berlin Heidelberg, 1987. http://dx.doi.org/10.1007/3-540-18081-8.

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14

Jantke, Klaus P., ed. Analogical and Inductive Inference. Springer Berlin Heidelberg, 1989. http://dx.doi.org/10.1007/3-540-51734-0.

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15

International, Workshop AII '86 (1986 Wendisch-Rietz Germany). Analogical and inductive inference: Proceedings. Springer-Verlag, 1987.

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16

Silva, Catarina, and Bernardete Ribeiro. Inductive Inference for Large Scale Text Classification. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-04533-2.

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17

International, Workshop on Analogical and Inductive Inference (1986 Wendisch-Rietz GDR). Analogical and inductive inference: International Workshop AII '86 Wendisch-Rietz, GDR, October 6-10, 1986 : proceedings. Springer-Verlag, 1987.

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18

P, Jantke K., ed. Analogical and inductive inference: International Workshop AII '92, Dagstuhl Castle, Germany, October 1992 : proceedings. Springer-Verlag, 1992.

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19

P, Jantke K., ed. Analogical and inductive inference: International Workshop AII '89, Reinhardsbrunn Castle, GDR, October, 1989 : proceedings. Springer-Verlag, 1989.

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20

Silva, Catarina. Inductive inference for large scale text classification: Kernel approaches and techniques. Springer, 2010.

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21

1941-, Arikawa S., Jantke K. P, and ALT '94 (1994 : Schloss Reinhardsbrunn), eds. Algorithmic learning theory: 4th International Workshop on Analogical and Inductive Inference, AII '94, 5th International Workshop on Algorithmic Learning Theory, ALT '94, Reinhardsbrunn Castle, Germany, October 10-15, 1994 : proceedings. Springer-Verlag, 1994.

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22

Shaw, Michael J. Rule learning in knowledge-based decision support systems: An integration of problem solving and inductive inference. College of Commerce and Business Administration, University of Illinois at Urbana-Champaign, 1987.

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23

Sandrini, Maria Grazia. L' inferenza induttiva in Bayes e in Fisher: Due metodi a confronto in un saggio storico-critico di epistemologia e metodologia scientifica. F. Angeli, 1987.

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24

Fuzziness and probability: An essay on the foundational nexus among semantics, measurement, uncertainty, and inductive and deductive inference, with application to decision analysis under uncertainty. ACG Press, 1995.

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25

Sprenger, Jan. Confirmation and Induction. Edited by Paul Humphreys. Oxford University Press, 2016. http://dx.doi.org/10.1093/oxfordhb/9780199368815.013.10.

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Scientific knowledge is based on induction, ampliative inferences from experience. The chapter gives an overview of the problem of induction and the responses that philosophers of science have developed over time, focusing on attempts to spell out rules of inductive inference, and to balance attractive theoretical principles with judgments and intuitions in particular cases. That this is not always easy is demonstrated by challenges such as the paradox of the ravens, the problem of irrelevant conjunctions, and Goodman's new riddle of induction. The chapter then focuses on explications of the degree of confirmation of a hypothesis and compares various Bayesian measures of confirmation, as well as the Bayesian and frequentist approaches to statistical inference.

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26

Holland, John H., Keith J. Holyoak, Paul R. Thagard, and Richard E. Nisbett. Induction: Processes of Inference, Learning, and Discovery. The MIT Press, 1989.

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27

1929-, Holland John H., ed. Induction: Processes of inference, learning, and discovery. MIT Press, 1986.

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28

Argument and Inference: An Introduction to Inductive Logic. The MIT Press, 2017.

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29

Johnson, Gregory. Argument and Inference: An Introduction to Inductive Logic. MIT Press, 2017.

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30

Johnson, Gregory. Argument and Inference: An Introduction to Inductive Logic. MIT Press, 2017.

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31

Inductive Arguments: Developing Critical Thinking Skills. 3rd ed. Kendall/Hunt Publishing Company, 1994.

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32

Johnsen, Bredo. Nelson Goodman. Oxford University Press, 2017. http://dx.doi.org/10.1093/acprof:oso/9780190662776.003.0008.

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Goodman addressed the problem of induction twice. His first approach is famous, centers on his “new riddle of induction,” and is the locus classicus of modern reflective equilibrium theory. In it the focus is on inductive inferences and rules of inductive inference. In his second approach, the focus is instead on the conclusions of inductive inferences to explanations of the available data. Here reflective equilibrium theory is more fully developed. The author in this chapter argues that Goodman’s two accounts of inductive justification in terms of reflective equilibrium share a deep commonality.

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33

Weintraub, Ruth. Scepticism about Inference to the Best Explanation. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198746904.003.0012.

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Scepticism about inference to the best explanation is far less often discussed than scepticism about another ampliative form of inference, enumerative induction. Both of these inference forms are widely used, and scepticism about either can pose an important challenge. This chapter aims to redress the imbalance by giving scepticism about inference to the best explanation the attention it, too, deserves. The chapter’s conclusion is that inference to the best explanation, even to the observable, may be in a worse epistemic position than enumerative induction. The reason for this is that there are sceptical arguments that target inference to the best explanation which do not have inductive analogues, but the converse is not true.

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34

Okasha, Samir. 2. Scientific reasoning. Oxford University Press, 2013. http://dx.doi.org/10.1093/actrade/9780192802835.003.0002.

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‘Scientific reasoning’ asks how much confidence should be placed in the inferences scientists make. What exactly is the nature of scientific reasoning? The important distinction between deductive and inductive patterns of reasoning is explained before Hume's problem is outlined. Science relies on induction and Hume's argument seems to show that induction cannot be rationally justified. Darwin's theory of evolution and Einstein's work on Brownian motion are provided as examples of inference to the best explanation. Finally, the interest of philosophers of science in probability and induction is shown and the frequency, subjective, and logical interpretations of probability are described.

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35

Earman, John. Inference, Explanation, and Other Frustrations: Essays in the Philosophy of Science. University of California Press, 2021.

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36

John, Earman, ed. Inference, explanation, and other frustrations: Essays in the philosophy of science. University of California Press, 1992.

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37

Earman, John. Inference, Explanation, and Other Frustrations: Essays in the Philosophy of Science. University of California Press, 2021.

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38

Induction: Processes of Inference, Learning and Discovery (Computational Models of Cognition and Perception). Bradford Book, 1986.

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39

Levi, Isaac. For the Sake of the Argument: Ramsey Test Conditionals, Inductive Inference and Nonmonotonic Reasoning. Cambridge University Press, 2007.

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40

Griffiths, Thomas L. Formalizing Prior Knowledge in Causal Induction. Edited by Michael R. Waldmann. Oxford University Press, 2017. http://dx.doi.org/10.1093/oxfordhb/9780199399550.013.38.

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Prior knowledge plays a central role in causal induction, helping to explain how people are capable of identifying causal relationships from small amounts of data. Bayesian inference provides a way to characterize the influence that prior knowledge should have on causal induction, as well as an explanation for how that knowledge could itself be acquired. Using the theory-based causal induction framework of Griffiths and Tenenbaum (2009), this chapter reviews recent work exploring the relationship between prior knowledge and causal induction, highlighting some of the ways in which people’s expectations about causal relationships differ from approaches to causal learning in statistics and computer science.

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41

Kornblith, Hilary. Inductive Inference and Its Natural Ground: An Essay in Naturalistic Epistemology. The MIT Press, 1995.

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42

Statistical and Inductive Inference by Minimum Message Length (Information Science and Statistics). Springer, 2005.

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43

Pollock, John L. Nomic Probability and the Foundations of Induction. Oxford University Press, 1990. http://dx.doi.org/10.1093/oso/9780195060133.001.0001.

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In this book Pollock deals with the subject of probabilistic reasoning, making general philosophical sense of objective probabilities and exploring their relationship to the problem of induction. He argues that probability is fundamental not only to physical science, but to induction, epistemology, the philosophy of science and much of the reasoning relevant to artificial intelligence. Pollock's main claim is that the fundamental notion of probability is nomic--that is, it involves the notion of natural law, valid across possible worlds. The various epistemic and statistical conceptions of probability, he demonstrates, are derived from this nomic notion. He goes on to provide a theory of statistical induction, an account of computational principles allowing some probabilities to be derived from others, an account of acceptance rules, and a theory of direct inference.

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44

Pólya, George. Mathematics and Plausible Reasoning, V1-2: Induction and Analogy in Mathematics, Patterns of Plausible Inference. Literary Licensing, LLC, 2012.

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45

Walczak, Steven Michael. Using inductive inference of past performance to build strategic cognitive adversary models. 1990.

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46

The Design Inference: Eliminating Chance through Small Probabilities (Cambridge Studies in Probability, Induction and Decision Theory). Cambridge University Press, 2006.

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47

Scherer, Klaus, Marcello Mortillaro, and Marc Mehu. Facial Expression Is Driven by Appraisal and Generates Appraisal Inference. Oxford University Press, 2017. http://dx.doi.org/10.1093/acprof:oso/9780190613501.003.0019.

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Emotion researchers generally concur that most emotions in humans and animals are elicited by the appraisals of events that are highly relevant for the organism, generating action tendencies that are often accompanied by changes in expression, autonomic physiology, and feeling. Scherer’s component process model of emotion (CPM) postulates that individual appraisal checks drive the dynamics and configuration of the facial expression of emotion and that emotion recognition is based on appraisal inference with consequent emotion attribution. This chapter outlines the model and reviews the accrued empirical evidence that supports these claims, covering studies that experimentally induced specific appraisals or that used induction of emotions with typical appraisal configurations (measuring facial expression via electromyographic recording) or behavioral coding of facial action units. In addition, recent studies analyzing the mechanisms of emotion recognition are shown to support the theoretical assumptions.

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48

Nicholas, Rescher. Cognitive Harmony: The Role of Systemic Harmony in the Constitution of Knowledge. University of Pittsburgh Press, 2005.

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49

Johnson, Samuel G. B., and Woo-kyoung Ahn. Causal Mechanisms. Edited by Michael R. Waldmann. Oxford University Press, 2017. http://dx.doi.org/10.1093/oxfordhb/9780199399550.013.12.

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This chapter reviews empirical and theoretical results concerning knowledge of causal mechanisms—beliefs about how and why events are causally linked. First, it reviews the effects of mechanism knowledge, showing that mechanism knowledge can override other cues to causality (including covariation evidence and temporal cues) and structural constraints (the Markov condition), and that mechanisms play a key role in various forms of inductive inference. Second, it examines several theories of how mechanisms are mentally represented—as associations, forces or powers, icons, abstract placeholders, networks, or schemas—and the empirical evidence bearing on each theory. Finally, it describes ways that people acquire mechanism knowledge, discussing the contributions from statistical induction, testimony, reasoning, and perception. For each of these topics, it highlights key open questions for future research.

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50

The Emergence of Probability: A Philosophical Study of Early Ideas about Probability, Induction and Statistical Inference (Cambridge Series on Statistical and Probabilistic Mathematic). 2nd ed. Cambridge University Press, 2006.

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