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Bayesian Philosophy of Science

Oxford and New York: Oxford University Press (2019)

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  1. Bayesian Epistemology.Luc Bovens & Stephan Hartmann - 2003 - Oxford: Oxford University Press. Edited by Stephan Hartmann.
    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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  • (1 other version)Studies in the logic of explanation.Carl Gustav Hempel & Paul Oppenheim - 1948 - Philosophy of Science 15 (2):135-175.
    To explain the phenomena in the world of our experience, to answer the question “why?” rather than only the question “what?”, is one of the foremost objectives of all rational inquiry; and especially, scientific research in its various branches strives to go beyond a mere description of its subject matter by providing an explanation of the phenomena it investigates. While there is rather general agreement about this chief objective of science, there exists considerable difference of opinion as to the function (...)
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  • Explanation and scientific understanding.Michael Friedman - 1974 - Journal of Philosophy 71 (1):5-19.
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  • The inference to the best explanation.Gilbert H. Harman - 1965 - Philosophical Review 74 (1):88-95.
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  • On conditionals.Dorothy Edgington - 1995 - Mind 104 (414):235-329.
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  • Inductive risk and values in science.Heather Douglas - 2000 - Philosophy of Science 67 (4):559-579.
    Although epistemic values have become widely accepted as part of scientific reasoning, non-epistemic values have been largely relegated to the "external" parts of science (the selection of hypotheses, restrictions on methodologies, and the use of scientific technologies). I argue that because of inductive risk, or the risk of error, non-epistemic values are required in science wherever non-epistemic consequences of error should be considered. I use examples from dioxin studies to illustrate how non-epistemic consequences of error can and should be considered (...)
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  • Reduction, qualia and the direct introspection of brain states.Paul M. Churchland - 1985 - Journal of Philosophy 82 (January):8-28.
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  • Cause and Norm.Christopher Hitchcock & Joshua Knobe - 2009 - Journal of Philosophy 106 (11):587-612.
    Much of the philosophical literature on causation has focused on the concept of actual causation, sometimes called token causation. In particular, it is this notion of actual causation that many philosophical theories of causation have attempted to capture.2 In this paper, we address the question: what purpose does this concept serve? As we shall see in the next section, one does not need this concept for purposes of prediction or rational deliberation. What then could the purpose be? We will argue (...)
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  • Studies in the logic of confirmation (I.).Carl Gustav Hempel - 1945 - Mind 54 (213):1-26.
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  • Bayesian Cognitive Science, Unification, and Explanation.Stephan Hartmann & Matteo Colombo - 2017 - British Journal for the Philosophy of Science 68 (2).
    It is often claimed that the greatest value of the Bayesian framework in cognitive science consists in its unifying power. Several Bayesian cognitive scientists assume that unification is obviously linked to explanatory power. But this link is not obvious, as unification in science is a heterogeneous notion, which may have little to do with explanation. While a crucial feature of most adequate explanations in cognitive science is that they reveal aspects of the causal mechanism that produces the phenomenon to be (...)
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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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  • Who’s Afraid of Nagelian Reduction?Foad Dizadji-Bahmani, Roman Frigg & Stephan Hartmann - 2010 - Erkenntnis 73 (3):393-412.
    We reconsider the Nagelian theory of reduction and argue that, contrary to a widely held view, it is the right analysis of intertheoretic reduction. The alleged difficulties of the theory either vanish upon closer inspection or turn out to be substantive philosophical questions rather than knock-down arguments.
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  • How to Tell When Simpler, More Unified, or Less A d Hoc Theories Will Provide More Accurate Predictions.Malcolm R. Forster & Elliott Sober - 1994 - British Journal for the Philosophy of Science 45 (1):1-35.
    Traditional analyses of the curve fitting problem maintain that the data do not indicate what form the fitted curve should take. Rather, this issue is said to be settled by prior probabilities, by simplicity, or by a background theory. In this paper, we describe a result due to Akaike [1973], which shows how the data can underwrite an inference concerning the curve's form based on an estimate of how predictively accurate it will be. We argue that this approach throws light (...)
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  • Justifying conditionalization: Conditionalization maximizes expected epistemic utility.Hilary Greaves & David Wallace - 2006 - Mind 115 (459):607-632.
    According to Bayesian epistemology, the epistemically rational agent updates her beliefs by conditionalization: that is, her posterior subjective probability after taking account of evidence X, pnew, is to be set equal to her prior conditional probability pold(·|X). Bayesians can be challenged to provide a justification for their claim that conditionalization is recommended by rationality—whence the normative force of the injunction to conditionalize? There are several existing justifications for conditionalization, but none directly addresses the idea that conditionalization will be epistemically rational (...)
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  • Graded Causation and Defaults.Joseph Y. Halpern & Christopher Hitchcock - 2015 - British Journal for the Philosophy of Science 66 (2):413-457.
    Recent work in psychology and experimental philosophy has shown that judgments of actual causation are often influenced by consideration of defaults, typicality, and normality. A number of philosophers and computer scientists have also suggested that an appeal to such factors can help deal with problems facing existing accounts of actual causation. This article develops a flexible formal framework for incorporating defaults, typicality, and normality into an account of actual causation. The resulting account takes actual causation to be both graded and (...)
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  • The No Alternatives Argument.Richard Dawid, Stephan Hartmann & Jan Sprenger - 2015 - British Journal for the Philosophy of Science 66 (1):213-234.
    Scientific theories are hard to find, and once scientists have found a theory, H, they often believe that there are not many distinct alternatives to H. But is this belief justified? What should scientists believe about the number of alternatives to H, and how should they change these beliefs in the light of new evidence? These are some of the questions that we will address in this article. We also ask under which conditions failure to find an alternative to H (...)
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  • Dr. Truthlove or: How I Learned to Stop Worrying and Love Bayesian Probabilities.Kenny Easwaran - 2016 - Noûs 50 (4):816-853.
    Many philosophers have argued that "degree of belief" or "credence" is a more fundamental state grounding belief. Many other philosophers have been skeptical about the notion of "degree of belief", and take belief to be the only meaningful notion in the vicinity. This paper shows that one can take belief to be fundamental, and ground a notion of "degree of belief" in the patterns of belief, assuming that an agent has a collection of beliefs that isn't dominated by some other (...)
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  • The plurality of bayesian measures of confirmation and the problem of measure sensitivity.Branden Fitelson - 1999 - Philosophy of Science 66 (3):378.
    Contemporary Bayesian confirmation theorists measure degree of (incremental) confirmation using a variety of non-equivalent relevance measures. As a result, a great many of the arguments surrounding quantitative Bayesian confirmation theory are implicitly sensitive to choice of measure of confirmation. Such arguments are enthymematic, since they tacitly presuppose that certain relevance measures should be used (for various purposes) rather than other relevance measures that have been proposed and defended in the philosophical literature. I present a survey of this pervasive class of (...)
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  • Analytic epistemology and experimental philosophy.Joshua Alexander & Jonathan M. Weinberg - 2006 - Philosophy Compass 2 (1):56–80.
    It has been standard philosophical practice in analytic philosophy to employ intuitions generated in response to thought-experiments as evidence in the evaluation of philosophical claims. In part as a response to this practice, an exciting new movement—experimental philosophy—has recently emerged. This movement is unified behind both a common methodology and a common aim: the application of methods of experimental psychology to the study of the nature of intuitions. In this paper, we will introduce two different views concerning the relationship that (...)
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  • Studies in the logic of confirmation (II.).Carl Gustav Hempel - 1945 - Mind 54 (214):97-121.
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  • (1 other version)The communication structure of epistemic communities.Kevin J. S. Zollman - 2007 - Philosophy of Science 74 (5):574-587.
    Increasingly, epistemologists are becoming interested in social structures and their effect on epistemic enterprises, but little attention has been paid to the proper distribution of experimental results among scientists. This paper will analyze a model first suggested by two economists, which nicely captures one type of learning situation faced by scientists. The results of a computer simulation study of this model provide two interesting conclusions. First, in some contexts, a community of scientists is, as a whole, more reliable when its (...)
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  • (1 other version)Causes and explanations: A structural-model approach. Part I: Causes.Joseph Y. Halpern & Judea Pearl - 2005 - British Journal for the Philosophy of Science 56 (4):843-887.
    We propose a new definition of actual causes, using structural equations to model counterfactuals. We show that the definition yields a plausible and elegant account of causation that handles well examples which have caused problems for other definitions and resolves major difficulties in the traditional account.
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  • On the current status of the issue of scientific realism.Richard Boyd - 1983 - Erkenntnis 19 (1-3):45 - 90.
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  • The Value of Cognitive Values.Heather Douglas - 2013 - Philosophy of Science 80 (5):796-806.
    Traditionally, cognitive values have been thought of as a collective pool of considerations in science that frequently trade against each other. I argue here that a finer-grained account of the value of cognitive values can help reduce such tensions. I separate the values into groups, minimal epistemic criteria, pragmatic considerations, and genuine epistemic assurance, based in part on the distinction between values that describe theories per se and values that describe theory-evidence relationships. This allows us to clarify why these values (...)
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  • Prediction versus accommodation and the risk of overfitting.Christopher Hitchcock & Elliott Sober - 2004 - British Journal for the Philosophy of Science 55 (1):1-34.
    an observation to formulate a theory, it is no surprise that the resulting theory accurately captures that observation. However, when the theory makes a novel prediction—when it predicts an observation that was not used in its formulation—this seems to provide more substantial confirmation of the theory. This paper presents a new approach to the vexed problem of understanding the epistemic difference between prediction and accommodation. In fact, there are several problems that need to be disentangled; in all of them, the (...)
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  • Measuring confirmation.David Christensen - 1999 - Journal of Philosophy 96 (9):437-461.
    The old evidence problem affects any probabilistic confirmation measure based on comparing pr(H/E) and pr(H). The article argues for the following points: (1) measures based on likelihood ratios also suffer old evidence difficulties; (2) the less-discussed synchronic old evidence problem is, in an important sense, the most acute; (3) prominent attempts to solve or dissolve the synchronic problem fail; (4) a little-discussed variant of the standard measure avoids the problem, in an appealing way; and (5) this measure nevertheless reveals a (...)
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  • Measuring Causal Specificity.Paul E. Griffiths, Arnaud Pocheville, Brett Calcott, Karola Stotz, Hyunju Kim & Rob Knight - 2015 - Philosophy of Science 82 (4):529-555.
    Several authors have argued that causes differ in the degree to which they are ‘specific’ to their effects. Woodward has used this idea to enrich his influential interventionist theory of causal explanation. Here we propose a way to measure causal specificity using tools from information theory. We show that the specificity of a causal variable is not well-defined without a probability distribution over the states of that variable. We demonstrate the tractability and interest of our proposed measure by measuring the (...)
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  • The current status of scientific realism.Richard Boyd - 1984 - In Jarrett Leplin (ed.), Scientific Realism. University of California Press. pp. 195--222.
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  • The Irreducible Complexity of Objectivity.Heather Douglas - 2004 - Synthese 138 (3):453 - 473.
    The terms ``objectivity'''' and ``objective'''' are among the mostused yet ill-defined terms in the philosophy of science and epistemology. Common to all thevarious usages is the rhetorical force of ``I endorse this and you should too'''', orto put it more mildly, that one should trust the outcome of the objectivity-producing process.The persuasive endorsement and call to trust provide some conceptual coherenceto objectivity, but the reference to objectivity is hopefully not merely an attemptat persuasive endorsement. What, in addition to epistemological endorsement,does (...)
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  • Explanatory generalizations, part II: Plumbing explanatory depth.Christopher Hitchcock & James Woodward - 2003 - Noûs 37 (2):181–199.
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  • (1 other version)Symmetries and asymmetries in evidential support.Ellery Eells & Branden Fitelson - 2002 - Philosophical Studies 107 (2):129 - 142.
    Several forms of symmetry in degrees of evidential support areconsidered. Some of these symmetries are shown not to hold in general. This has implications for the adequacy of many measures of degree ofevidential support that have been proposed and defended in the philosophical literature.
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  • On bayesian measures of evidential support: Theoretical and empirical issues.Vincenzo Crupi, Katya Tentori & and Michel Gonzalez - 2007 - Philosophy of Science 74 (2):229-252.
    Epistemologists and philosophers of science have often attempted to express formally the impact of a piece of evidence on the credibility of a hypothesis. In this paper we will focus on the Bayesian approach to evidential support. We will propose a new formal treatment of the notion of degree of confirmation and we will argue that it overcomes some limitations of the currently available approaches on two grounds: (i) a theoretical analysis of the confirmation relation seen as an extension of (...)
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  • Communism and the Incentive to Share in Science.Remco Heesen - 2017 - Philosophy of Science 84 (4):698-716.
    The communist norm requires that scientists widely share the results of their work. Where did this norm come from, and how does it persist? Michael Strevens provides a partial answer to these questions by showing that scientists should be willing to sign a social contract that mandates sharing. However, he also argues that it is not in an individual credit-maximizing scientist's interest to follow this norm. I argue against Strevens that individual scientists can rationally conform to the communist norm, even (...)
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  • Learning from Conditionals.Benjamin Eva, Stephan Hartmann & Soroush Rafiee Rad - 2020 - Mind 129 (514):461-508.
    In this article, we address a major outstanding question of probabilistic Bayesian epistemology: how should a rational Bayesian agent update their beliefs upon learning an indicative conditional? A number of authors have recently contended that this question is fundamentally underdetermined by Bayesian norms, and hence that there is no single update procedure that rational agents are obliged to follow upon learning an indicative conditional. Here we resist this trend and argue that a core set of widely accepted Bayesian norms is (...)
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  • A principled approach to defining actual causation.Sander Beckers & Joost Vennekens - 2018 - Synthese 195 (2):835-862.
    In this paper we present a new proposal for defining actual causation, i.e., the problem of deciding if one event caused another. We do so within the popular counterfactual tradition initiated by Lewis, which is characterised by attributing a fundamental role to counterfactual dependence. Unlike the currently prominent definitions, our approach proceeds from the ground up: we start from basic principles, and construct a definition of causation that satisfies them. We define the concepts of counterfactual dependence and production, and put (...)
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  • The Best Humean System for Statistical Mechanics.Roman Frigg & Carl Hoefer - 2015 - Erkenntnis 80 (S3):551-574.
    Classical statistical mechanics posits probabilities for various events to occur, and these probabilities seem to be objective chances. This does not seem to sit well with the fact that the theory’s time evolution is deterministic. We argue that the tension between the two is only apparent. We present a theory of Humean objective chance and show that chances thus understood are compatible with underlying determinism and provide an interpretation of the probabilities we find in Boltzmannian statistical mechanics.
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  • The Problem of Measure Sensitivity Redux.Peter Brössel - 2013 - Philosophy of Science 80 (3):378-397.
    Fitelson (1999) demonstrates that the validity of various arguments within Bayesian confirmation theory depends on which confirmation measure is adopted. The present paper adds to the results set out in Fitelson (1999), expanding on them in two principal respects. First, it considers more confirmation measures. Second, it shows that there are important arguments within Bayesian confirmation theory and that there is no confirmation measure that renders them all valid. Finally, the paper reviews the ramifications that this "strengthened problem of measure (...)
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  • (1 other version)Arguments For—Or Against—Probabilism?Alan Hájek - 2009 - In Franz Huber & Christoph Schmidt-Petri (eds.), Degrees of belief. London: Springer. pp. 229--251.
    Four important arguments for probabilism—the Dutch Book, representation theorem, calibration, and gradational accuracy arguments—have a strikingly similar structure. Each begins with a mathematical theorem, a conditional with an existentially quantified consequent, of the general form: if your credences are not probabilities, then there is a way in which your rationality is impugned. Each argument concludes that rationality requires your credences to be probabilities. I contend that each argument is invalid as formulated. In each case there is a mirror-image theorem and (...)
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  • How the growth of science ends theory change.Ludwig Fahrbach - 2011 - Synthese 180 (2):139-155.
    This paper outlines a defense of scientific realism against the pessimistic meta- induction which appeals to the phenomenon of the exponential growth of science. Here, scientific realism is defined as the view that our current successful scientific theories are mostly approximately true, and pessimistic meta- induction is the argument that projects the occurrence of past refutations of successful theories to the present concluding that many or most current successful scientific theories are false. The defense starts with the observation that at (...)
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  • Inductive inconsistencies.Carl Gustav Hempel - 1960 - Synthese 12 (4):439-69.
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  • Absence of evidence and evidence of absence: evidential transitivity in connection with fossils, fishing, fine-tuning, and firing squads.Elliott Sober - 2009 - Philosophical Studies 143 (1):63-90.
    “Absence of evidence isn’t evidence of absence” is a slogan that is popular among scientists and nonscientists alike. This article assesses its truth by using a probabilistic tool, the Law of Likelihood. Qualitative questions (“Is E evidence about H ?”) and quantitative questions (“How much evidence does E provide about H ?”) are both considered. The article discusses the example of fossil intermediates. If finding a fossil that is phenotypically intermediate between two extant species provides evidence that those species have (...)
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  • Bayesianism I: Introduction and Arguments in Favor.Kenny Easwaran - 2011 - Philosophy Compass 6 (5):312-320.
    Bayesianism is a collection of positions in several related fields, centered on the interpretation of probability as something like degree of belief, as contrasted with relative frequency, or objective chance. However, Bayesianism is far from a unified movement. Bayesians are divided about the nature of the probability functions they discuss; about the normative force of this probability function for ordinary and scientific reasoning and decision making; and about what relation (if any) holds between Bayesian and non-Bayesian concepts.
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  • Reintroducing prediction to explanation.Heather E. Douglas - 2009 - Philosophy of Science 76 (4):444-463.
    Although prediction has been largely absent from discussions of explanation for the past 40 years, theories of explanation can gain much from a reintroduction. I review the history that divorced prediction from explanation, examine the proliferation of models of explanation that followed, and argue that accounts of explanation have been impoverished by the neglect of prediction. Instead of a revival of the symmetry thesis, I suggest that explanation should be understood as a cognitive tool that assists us in generating new (...)
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  • The No Miracles Argument without the Base Rate Fallacy.Richard Dawid & Stephan Hartmann - 2016 - Synthese 195 (9):4063-4079.
    According to an argument by Colin Howson, the no-miracles argument is contingent on committing the base-rate fallacy and is therefore bound to fail. We demonstrate that Howson’s argument only applies to one of two versions of the NMA. The other version, which resembles the form in which the argument was initially presented by Putnam and Boyd, remains unaffected by his line of reasoning. We provide a formal reconstruction of that version of the NMA and show that it is valid. Finally, (...)
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  • Confirmation and Reduction: a Bayesian Account.Foad Dizadji-Bahmani, Roman Frigg & Stephan Hartmann - 2010 - Synthese 179 (2):321-338.
    Various scientific theories stand in a reductive relation to each other. In a recent article, we have argued that a generalized version of the Nagel-Schaffner model (GNS) is the right account of this relation. In this article, we present a Bayesian analysis of how GNS impacts on confirmation. We formalize the relation between the reducing and the reduced theory before and after the reduction using Bayesian networks, and thereby show that, post-reduction, the two theories are confirmatory of each other. We (...)
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  • (1 other version)A Bayesian Account of Independent Evidence with Applications.Branden Fitelson - 2001 - Philosophy of Science 68 (S3):S123-S140.
    A Bayesian account of independent evidential support is outlined. This account is partly inspired by the work of C. S. Peirce. I show that a large class of quantitative Bayesian measures of confirmation satisfy some basic desiderata suggested by Peirce for adequate accounts of independent evidence. I argue that, by considering further natural constraints on a probabilistic account of independent evidence, all but a very small class of Bayesian measures of confirmation can be ruled out. In closing, another application of (...)
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  • Bayesianism II: Applications and Criticisms.Kenny Easwaran - 2011 - Philosophy Compass 6 (5):321-332.
    In the first paper, I discussed the basic claims of Bayesianism (that degrees of belief are important, that they obey the axioms of probability theory, and that they are rationally updated by either standard or Jeffrey conditionalization) and the arguments that are often used to support them. In this paper, I will discuss some applications these ideas have had in confirmation theory, epistemol- ogy, and statistics, and criticisms of these applications.
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  • Objective probability as a guide to the world.Michael Strevens - 1999 - Philosophical Studies 95 (3):243-275.
    According to principles of probability coordination, such as Miller's Principle or Lewis's Principal Principle, you ought to set your subjective probability for an event equal to what you take to be the objective probability of the event. For example, you should expect events with a very high probability to occur and those with a very low probability not to occur. This paper examines the grounds of such principles. It is argued that any attempt to justify a principle of probability coordination (...)
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  • The Structural Evolution of Morality.J. McKenzie Alexander - 2007 - New York: Cambridge University Press.
    It is certainly the case that morality governs the interactions that take place between individuals. But what if morality exists because of these interactions? This book, first published in 2007, argues for the claim that much of the behaviour we view as 'moral' exists because acting in that way benefits each of us to the greatest extent possible, given the socially structured nature of society. Drawing upon aspects of evolutionary game theory, the theory of bounded rationality, and computational models of (...)
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  • Declarations of independence.Branden Fitelson & Alan Hájek - 2017 - Synthese 194 (10):3979-3995.
    According to orthodox (Kolmogorovian) probability theory, conditional probabilities are by definition certain ratios of unconditional probabilities. As a result, orthodox conditional probabilities are undefined whenever their antecedents have zero unconditional probability. This has important ramifications for the notion of probabilistic independence. Traditionally, independence is defined in terms of unconditional probabilities (the factorization of the relevant joint unconditional probabilities). Various “equivalent” formulations of independence can be given using conditional probabilities. But these “equivalences” break down if conditional probabilities are permitted to have (...)
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