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  1. Grounding in the Image of Causation.Jonathan Schaffer - 2016 - Philosophical Studies 173 (1):49-100.
    Grounding is often glossed as metaphysical causation, yet no current theory of grounding looks remotely like a plausible treatment of causation. I propose to take the analogy between grounding and causation seriously, by providing an account of grounding in the image of causation, on the template of structural equation models for causation.
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  • Bayesian Nets and Causality: Philosophical and Computational Foundations.Jon Williamson - 2004 - Oxford, England: Oxford University Press.
    Bayesian nets are widely used in artificial intelligence as a calculus for causal reasoning, enabling machines to make predictions, perform diagnoses, take decisions and even to discover causal relationships. This book, aimed at researchers and graduate students in computer science, mathematics and philosophy, brings together two important research topics: how to automate reasoning in artificial intelligence, and the nature of causality and probability in philosophy.
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  • Inference to the Best Explanation.Peter Lipton - 1991 - London and New York: Routledge.
    How do we go about weighing evidence, testing hypotheses, and making inferences? According to the model of _Inference to the Best Explanation_, we work out what to infer from the evidence by thinking about what would actually explain that evidence, and we take the ability of a hypothesis to explain the evidence as a sign that the hypothesis is correct. In _Inference to the Best Explanation_, Peter Lipton gives this important and influential idea the development and assessment it deserves. The (...)
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  • The Logic of Decision.Richard C. Jeffrey - 1965 - New York, NY, USA: University of Chicago Press.
    "[This book] proposes new foundations for the Bayesian principle of rational action, and goes on to develop a new logic of desirability and probabtility."—Frederic Schick, _Journal of Philosophy_.
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  • Logical Foundations of Probability.Rudolf Carnap - 1950 - Chicago, IL, USA: Chicago University of Chicago Press.
    APA PsycNET abstract: This is the first volume of a two-volume work on Probability and Induction. Because the writer holds that probability logic is identical with inductive logic, this work is devoted to philosophical problems concerning the nature of probability and inductive reasoning. The author rejects a statistical frequency basis for probability in favor of a logical relation between two statements or propositions. Probability "is the degree of confirmation of a hypothesis (or conclusion) on the basis of some given evidence (...)
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  • Bayesian Epistemology.Luc Bovens & Stephan Hartmann - 2003 - Oxford: Oxford University Press.
    Probabilistic models have much to offer to philosophy. We continually receive information from a variety of sources: from our senses, from witnesses, from scientific instruments. When considering whether we should believe this information, we assess whether the sources are independent, how reliable they are, and how plausible and coherent the information is. Bovens and Hartmann provide a systematic Bayesian account of these features of reasoning. Simple Bayesian Networks allow us to model alternative assumptions about the nature of the information sources. (...)
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  • Inference to the Best Explanation Made Incoherent.Nevin Climenhaga - 2017 - Journal of Philosophy 114 (5):251-273.
    Defenders of Inference to the Best Explanation claim that explanatory factors should play an important role in empirical inference. They disagree, however, about how exactly to formulate this role. In particular, they disagree about whether to formulate IBE as an inference rule for full beliefs or for degrees of belief, as well as how a rule for degrees of belief should relate to Bayesianism. In this essay I advance a new argument against non-Bayesian versions of IBE. My argument focuses on (...)
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  • How Explanation Guides Confirmation.Nevin Climenhaga - 2017 - Philosophy of Science 84 (2):359-68.
    Where E is the proposition that [If H and O were true, H would explain O], William Roche and Elliot Sober have argued that P(H|O&E) = P(H|O). In this paper I argue that not only is this equality not generally true, it is false in the very kinds of cases that Roche and Sober focus on, involving frequency data. In fact, in such cases O raises the probability of H only given that there is an explanatory connection between them.
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  • The Concept of Inductive Probability.Patrick Maher - 2006 - Erkenntnis 65 (2):185-206.
    The word ‘probability’ in ordinary language has two different senses, here called inductive and physical probability. This paper examines the concept of inductive probability. Attempts to express this concept in other words are shown to be either incorrect or else trivial. In particular, inductive probability is not the same as degree of belief. It is argued that inductive probabilities exist; subjectivist arguments to the contrary are rebutted. Finally, it is argued that inductive probability is an important concept and that it (...)
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  • Transitivity, Self-Explanation, and the Explanatory Circularity Argument Against Humean Accounts of Natural Law.Marc Lange - 2018 - Synthese 195 (3):1337-1353.
    Humean accounts of natural lawhood have often been criticized as unable to account for the laws’ characteristic explanatory power in science. Loewer has replied that these criticisms fail to distinguish grounding explanations from scientific explanations. Lange has replied by arguing that grounding explanations and scientific explanations are linked by a transitivity principle, which can be used to argue that Humean accounts of natural law violate the prohibition on self-explanation. Lange’s argument has been sharply criticized by Hicks and van Elswyk, Marshall, (...)
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  • Probability Captures the Logic of Scientific Confirmation.Patrick Maher - 2004 - In Christopher Hitchcock (ed.), Contemporary Debates in Philosophy of Science. Blackwell. pp. 69--93.
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  • A Defense of Objectivism About Evidential Support.Brian Hedden - 2015 - Canadian Journal of Philosophy 45 (5):716-743.
    Objectivism about evidential support is the thesis that facts about the degree to which a body of evidence supports a hypothesis are objective rather than depending on subjective factors like one’s own language or epistemic values. Objectivism about evidential support is key to defending a synchronic, time-slice-centric conception of epistemic rationality, on which what you ought to believe at a time depends only on what evidence you have at that time, and not on how you were at previous times. Here, (...)
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  • The Structure and Dynamics of Scientific Theories: A Hierarchical Bayesian Perspective.L. Henderson, N. D. Goodman, J. B. Tenenbaum & J. F. Woodward - 2010 - Philosophy of Science 77 (2):172-200.
    Hierarchical Bayesian models (HBMs) provide an account of Bayesian inference in a hierarchically structured hypothesis space. Scientific theories are plausibly regarded as organized into hierarchies in many cases, with higher levels sometimes called ‘para- digms’ and lower levels encoding more specific or concrete hypotheses. Therefore, HBMs provide a useful model for scientific theory change, showing how higher-level theory change may be driven by the impact of evidence on lower levels. HBMs capture features described in the Kuhnian tradition, particularly the idea (...)
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  • The Best Explanation: Criteria for Theory Choice.Paul R. Thagard - 1978 - Journal of Philosophy 75 (2):76-92.
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  • Warrant and Proper Function.Alvin Plantinga - 1993 - Oxford University Press.
    In this companion volume to Warrant: The Current Debate, Plantinga develops an original approach to the question of epistemic warrant; that is what turns true belief into knowledge. He argues that what is crucial to warrant is the proper functioning of one's cognitive faculties in the right kind of cognitive environment.
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  • Artificial Intelligence: A Modern Approach.Stuart Jonathan Russell & Peter Norvig (eds.) - 1995 - Prentice-Hall.
    Artificial Intelligence: A Modern Approach, 3e offers the most comprehensive, up-to-date introduction to the theory and practice of artificial intelligence. Number one in its field, this textbook is ideal for one or two-semester, undergraduate or graduate-level courses in Artificial Intelligence. Dr. Peter Norvig, contributing Artificial Intelligence author and Professor Sebastian Thrun, a Pearson author are offering a free online course at Stanford University on artificial intelligence. According to an article in The New York Times, the course on artificial intelligence is (...)
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  • Inference to the Best Explanation.Peter Lipton - 2007 - Philosophy and Phenomenological Research 74 (2):421-423.
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  • In Defence of Objective Bayesianism.Jon Williamson - 2010 - Oxford University Press.
    Objective Bayesianism is a methodological theory that is currently applied in statistics, philosophy, artificial intelligence, physics and other sciences. This book develops the formal and philosophical foundations of the theory, at a level accessible to a graduate student with some familiarity with mathematical notation.
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  • The Logic of Chance.John Venn - 1876 - Dover Publications.
    No mathematical background is necessary to appreciate this classic of probability theory, which remains unsurpassed in its clarity, readability, and sheer charm. Its author, British logician John Venn (1834-1923), popularized the famous Venn Diagrams that are commonly used in teaching elementary mathematics.
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  • Probability Theory. The Logic of Science.Edwin T. Jaynes - 2003 - Cambridge University Press: Cambridge.
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  • Knowledge and its Limits.Timothy Williamson - 2000 - Oxford University Press.
    Knowledge and its Limits presents a systematic new conception of knowledge as a kind of mental stage sensitive to the knower's environment. It makes a major contribution to the debate between externalist and internalist philosophies of mind, and breaks radically with the epistemological tradition of analyzing knowledge in terms of true belief. The theory casts new light on such philosophical problems as scepticism, evidence, probability and assertion, realism and anti-realism, and the limits of what can be known. The arguments are (...)
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  • Locating IBE in the Bayesian Framework.Jonathan Weisberg - 2009 - Synthese 167 (1):125-143.
    Inference to the Best Explanation (IBE) and Bayesianism are our two most prominent theories of scientific inference. Are they compatible? Van Fraassen famously argued that they are not, concluding that IBE must be wrong since Bayesianism is right. Writers since then, from both the Bayesian and explanationist camps, have usually considered van Fraassen’s argument to be misguided, and have plumped for the view that Bayesianism and IBE are actually compatible. I argue that van Fraassen’s argument is actually not so misguided, (...)
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  • Explanationist Aid for the Theory of Inductive Logic.Michael Huemer - 2009 - British Journal for the Philosophy of Science 60 (2):345-375.
    A central problem facing a probabilistic approach to the problem of induction is the difficulty of sufficiently constraining prior probabilities so as to yield the conclusion that induction is cogent. The Principle of Indifference, according to which alternatives are equiprobable when one has no grounds for preferring one over another, represents one way of addressing this problem; however, the Principle faces the well-known problem that multiple interpretations of it are possible, leading to incompatible conclusions. I propose a partial solution to (...)
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  • Bayesianism and Inference to the Best Explanation.Leah Henderson - 2014 - British Journal for the Philosophy of Science 65 (4):687-715.
    Two of the most influential theories about scientific inference are inference to the best explanation and Bayesianism. How are they related? Bas van Fraassen has claimed that IBE and Bayesianism are incompatible rival theories, as any probabilistic version of IBE would violate Bayesian conditionalization. In response, several authors have defended the view that IBE is compatible with Bayesian updating. They claim that the explanatory considerations in IBE are taken into account by the Bayesian because the Bayesian either does or should (...)
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  • Probabilistic Alternatives to Bayesianism: The Case of Explanationism.Igor Douven & Jonah N. Schupbach - 2015 - Frontiers in Psychology 6.
    There has been a probabilistic turn in contemporary cognitive science. Far and away, most of the work in this vein is Bayesian, at least in name. Coinciding with this development, philosophers have increasingly promoted Bayesianism as the best normative account of how humans ought to reason. In this paper, we make a push for exploring the probabilistic terrain outside of Bayesianism. Non-Bayesian, but still probabilistic, theories provide plausible competitors both to descriptive and normative Bayesian accounts. We argue for this general (...)
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  • Inference to the Best Explanation, Dutch Books, and Inaccuracy Minimisation.Igor Douven - 2013 - Philosophical Quarterly 63 (252):428-444.
    Bayesians have traditionally taken a dim view of the Inference to the Best Explanation, arguing that, if IBE is at variance with Bayes ' rule, then it runs afoul of the dynamic Dutch book argument. More recently, Bayes ' rule has been claimed to be superior on grounds of conduciveness to our epistemic goal. The present paper aims to show that neither of these arguments succeeds in undermining IBE.
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  • Causality: Models, Reasoning and Inference.Judea Pearl - 2000 - Cambridge University Press.
    Causality offers the first comprehensive coverage of causal analysis in many sciences, including recent advances using graphical methods. Pearl presents a unified account of the probabilistic, manipulative, counterfactual and structural approaches to causation, and devises simple mathematical tools for analyzing the relationships between causal connections, statistical associations, actions and observations. The book will open the way for including causal analysis in the standard curriculum of statistics, artificial intelligence, business, epidemiology, social science and economics.
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  • Scientific Reasoning: The Bayesian Approach.Peter Urbach & Colin Howson - 1993 - Open Court.
    Scientific reasoning is—and ought to be—conducted in accordance with the axioms of probability. This Bayesian view—so called because of the central role it accords to a theorem first proved by Thomas Bayes in the late eighteenth ...
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  • Bayes' Theorem.James Joyce - 2008 - Stanford Encyclopedia of Philosophy.
    Bayes' Theorem is a simple mathematical formula used for calculating conditional probabilities. It figures prominently in subjectivist or Bayesian approaches to epistemology, statistics, and inductive logic. Subjectivists, who maintain that rational belief is governed by the laws of probability, lean heavily on conditional probabilities in their theories of evidence and their models of empirical learning. Bayes' Theorem is central to these enterprises both because it simplifies the calculation of conditional probabilities and because it clarifies significant features of subjectivist position. Indeed, (...)
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  • What Conditional Probability Could Not Be.Alan Hájek - 2003 - Synthese 137 (3):273--323.
    Kolmogorov''s axiomatization of probability includes the familiarratio formula for conditional probability: 0).$$ " align="middle" border="0">.
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  • Bayes or Bust? A Critical Examination of Bayesian Confirmation Theory.John Earman - 1992 - MIT Press.
    There is currently no viable alternative to the Bayesian analysis of scientific inference, yet the available versions of Bayesianism fail to do justice to several aspects of the testing and confirmation of scientific hypotheses. Bayes or Bust? provides the first balanced treatment of the complex set of issues involved in this nagging conundrum in the philosophy of science. Both Bayesians and anti-Bayesians will find a wealth of new insights on topics ranging from Bayes’s original paper to contemporary formal learning theory.In (...)
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  • Bayes or Bust?John Earman - 1992 - Bradford.
    There is currently no viable alternative to the Bayesian analysis of scientific inference, yet the available versions of Bayesianism fail to do justice to several aspects of the testing and confirmation of scientific hypotheses. Bayes or Bust? provides the first balanced treatment of the complex set of issues involved in this nagging conundrum in the philosophy of science. Both Bayesians and anti-Bayesians will find a wealth of new insights on topics ranging from Bayes's original paper to contemporary formal learning theory. (...)
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  • Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference.Judea Pearl - 1988 - Morgan Kaufmann.
    The book can also be used as an excellent text for graduate-level courses in AI, operations research, or applied probability.
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  • Epistemic Justification.Richard Swinburne - 2001 - Oxford University Press.
    Richard Swinburne offers an original treatment of a question at the heart of epistemology: what makes a belief rational, or justified in holding? He maps the rival accounts of philosophers on epistemic justification ("internalist" and "externalist"), arguing that they are really accounts of different concepts. He distinguishes between synchronic justification (justification at a time) and diachronic justification (synchronic justification resulting from adequate investigation)--both internalist and externalist. He also argues that most kinds of justification are worth having because they are indicative (...)
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  • The Philosophy of Science: An Historical Anthology.Timothy McGrew, Marc Alspector-Kelly & Fritz Allhoff (eds.) - 2009 - Wiley-Blackwell.
    speaking there are only two sorts of opposition to be found here. One is the opposition between motion and rest, together with the opposition between ...
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  • The Structure of Scientific Inference.Mary Hesse - 1974 - [London]Macmillan.
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  • An Introduction to Probability and Inductive Logic.Ian Hacking - 2001 - Cambridge University Press.
    This is an introductory 2001 textbook on probability and induction written by one of the world's foremost philosophers of science. The book has been designed to offer maximal accessibility to the widest range of students and assumes no formal training in elementary symbolic logic. It offers a comprehensive course covering all basic definitions of induction and probability, and considers such topics as decision theory, Bayesianism, frequency ideas, and the philosophical problem of induction. The key features of this book are a (...)
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  • Knowledge and Its Limits.Timothy Williamson - 2005 - Philosophy and Phenomenological Research 70 (2):452-458.
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  • Review: The Grand Leap; Reviewed Work: Causation, Prediction, and Search. [REVIEW]Peter Spirtes, Clark Glymour & Richard Scheines - 1996 - British Journal for the Philosophy of Science 47 (1):113-123.
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  • Causality: Models, Reasoning and Inference.Judea Pearl - 2000 - Tijdschrift Voor Filosofie 64 (1):201-202.
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  • Knowledge and Its Limits.Timothy Williamson - 2003 - Philosophical Quarterly 53 (210):105-116.
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  • Intersubjective Probability and Confirmation Theory.Donald Gillies - 1991 - British Journal for the Philosophy of Science 42 (4):513-533.
    This paper introduces what is called the intersubjective interpretation of the probability calculus. Intersubjective probabilities are related to subjective probabilities, and the paper begins with a particular formulation of the familiar Dutch Book argument. This argument is then extended, in Section 3, to social groups, and this enables the concept of intersubjective probability to be introduced in Section 4. It is then argued that the intersubjective interpretation is the appropriate one for the probabilities which appear in confirmation theory whether of (...)
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  • The Structure of Scientific Inference, By M. B. Hesse. [REVIEW]Jon Dorling - 1975 - British Journal for the Philosophy of Science 26 (1):61-71.
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  • The Structure and Dynamics of Scientific Theories: A Hierarchical Bayesian Perspective.Leah Henderson, Noah D. Goodman, Joshua B. Tenenbaum & James F. Woodward - 2010 - Philosophy of Science 77 (2):172-200.
    Hierarchical Bayesian models (HBMs) provide an account of Bayesian inference in a hierarchically structured hypothesis space. Scientific theories are plausibly regarded as organized into hierarchies in many cases, with higher levels sometimes called ‘paradigms’ and lower levels encoding more specific or concrete hypotheses. Therefore, HBMs provide a useful model for scientific theory change, showing how higher‐level theory change may be driven by the impact of evidence on lower levels. HBMs capture features described in the Kuhnian tradition, particularly the idea that (...)
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  • Degree-of-Belief and Degree-of-Support: Why Bayesians Need Both Notions.James Hawthorne - 2005 - Mind 114 (454):277-320.
    I argue that Bayesians need two distinct notions of probability. We need the usual degree-of-belief notion that is central to the Bayesian account of rational decision. But Bayesians also need a separate notion of probability that represents the degree to which evidence supports hypotheses. Although degree-of-belief is well suited to the theory of rational decision, Bayesians have tried to apply it to the realm of hypothesis confirmation as well. This double duty leads to the problem of old evidence, a problem (...)
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  • Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference.Henry E. Kyburg - 1991 - Journal of Philosophy 88 (8):434-437.
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  • Logical Foundations of Probability.Arthur W. Burks - 1951 - Journal of Philosophy 48 (17):524-535.
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  • A Treatise on Probability.Harry T. Costello - 1923 - Journal of Philosophy 20 (11):301-306.
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  • Simplicity and Natural Theology.Paul Draper - 2016 - In Michael Bergmann & Jeffrey E. Brower (eds.), Reason and Faith: Themes From Swinburne. Oxford, UK: pp. 48-63.
    My project is to examine and critically discuss the role of simplicity in Swinburne’s probabilistic natural theology. After describing that role and the details of his theory of simplicity, I challenge Swinburne’s view that the criterion of simplicity is a fundamental criterion for evaluating causal explanations, proposing instead that what is right about that criterion can be derived from a more fundamental criterion of “coherence.” I close by exploring the implications of my proposal for Swinburne’s natural theology.
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  • Inference to the Best Explanation.Alexander Bird - 2007 - Philosophy and Phenomenological Research 74 (2):424-432.
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