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Direct inference

Journal of Philosophy 74 (1):5-29 (1977)

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  1. 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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  • Divisive conditioning: Further results on dilation.Timothy Herron, Teddy Seidenfeld & Larry Wasserman - 1997 - Philosophy of Science 64 (3):411-444.
    Conditioning can make imprecise probabilities uniformly more imprecise. We call this effect "dilation". In a previous paper (1993), Seidenfeld and Wasserman established some basic results about dilation. In this paper we further investigate dilation on several models. In particular, we consider conditions under which dilation persists under marginalization and we quantify the degree of dilation. We also show that dilation manifests itself asymptotically in certain robust Bayesian models and we characterize the rate at which dilation occurs.
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  • Can human irrationality be experimentally demonstrated?L. Jonathan Cohen - 1981 - Behavioral and Brain Sciences 4 (3):317-370.
    The object of this paper is to show why recent research in the psychology of deductive and probabilistic reasoning does not have.
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  • Direct inference and probabilistic accounts of induction.Jon Williamson - 2023 - Journal for General Philosophy of Science / Zeitschrift für Allgemeine Wissenschaftstheorie 54 (3):451-472.
    Schurz (2019, ch. 4) argues that probabilistic accounts of induction fail. In particular, he criticises probabilistic accounts of induction that appeal to direct inference principles, including subjective Bayesian approaches (e.g., Howson 2000) and objective Bayesian approaches (see, e.g., Williamson 2017). In this paper, I argue that Schurz’ preferred direct inference principle, namely Reichenbach’s Principle of the Narrowest Reference Class, faces formidable problems in a standard probabilistic setting. Furthermore, the main alternative direct inference principle, Lewis’ Principal Principle, is also hard to (...)
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  • No Double-Halfer Embarrassment: A Reply to Titelbaum.Joel Pust - 2023 - Analytic Philosophy 64 (3):346-354.
    “Double-halfers” think that throughout the Sleeping Beauty Scenario, Beauty ought to maintain a credence of 1/2 in the proposition that the fair coin toss governing the experimental protocol comes up heads. Titelbaum (2012) introduces a novel variation on the standard scenario, one involving an additional coin toss, and claims that the double-halfer is committed to the absurd and embarrassing result that Beauty’s credence in an indexical proposition concerning the outcome of a future fair coin toss is not 1/2. I argue (...)
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  • Evidentialism, Inertia, and Imprecise Probability.William Peden - forthcoming - The British Journal for the Philosophy of Science:1-23.
    Evidentialists say that a necessary condition of sound epistemic reasoning is that our beliefs reflect only our evidence. This thesis arguably conflicts with standard Bayesianism, due to the importance of prior probabilities in the latter. Some evidentialists have responded by modelling belief-states using imprecise probabilities (Joyce 2005). However, Roger White (2010) and Aron Vallinder (2018) argue that this Imprecise Bayesianism is incompatible with evidentialism due to “inertia”, where Imprecise Bayesian agents become stuck in a state of ambivalence towards hypotheses. Additionally, (...)
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  • Support for Geometric Pooling.Jean Baccelli & Rush T. Stewart - 2023 - Review of Symbolic Logic 16 (1):298-337.
    Supra-Bayesianism is the Bayesian response to learning the opinions of others. Probability pooling constitutes an alternative response. One natural question is whether there are cases where probability pooling gives the supra-Bayesian result. This has been called the problem of Bayes-compatibility for pooling functions. It is known that in a common prior setting, under standard assumptions, linear pooling cannot be nontrivially Bayes-compatible. We show by contrast that geometric pooling can be nontrivially Bayes-compatible. Indeed, we show that, under certain assumptions, geometric and (...)
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  • Epistemic Justice and the Principle of Total Evidence.Sherrilyn Roush - manuscript
    Epistemic injustice is injustice to a person qua knower. In one form of this phenomenon a speaker’s testimony is denied credence in a way that wrongs them. I argue that the received definition of this testimonial injustice relies too heavily on epistemic criteria that cannot explain why the moral concept of injustice should be invoked. I give an account of the nature of the wrong of epistemic injustice that has it depend not on the accuracy of judgments that are used (...)
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  • Knowledge, Evidence, and Naked Statistics.Sherrilyn Roush - 2023 - In Luis R. G. Oliveira (ed.), Externalism about Knowledge. Oxford: Oxford University Press.
    Many who think that naked statistical evidence alone is inadequate for a trial verdict think that use of probability is the problem, and something other than probability – knowledge, full belief, causal relations – is the solution. I argue that the issue of whether naked statistical evidence is weak can be formulated within the probabilistic idiom, as the question whether likelihoods or only posterior probabilities should be taken into account in our judgment of a case. This question also identifies a (...)
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  • Evidence and Inductive Inference.Nevin Climenhaga - 2024 - In Maria Lasonen-Aarnio & Clayton Littlejohn (eds.), The Routledge Handbook of the Philosophy of Evidence. New York, NY: Routledge. pp. 435-449.
    This chapter presents a typology of the different kinds of inductive inferences we can draw from our evidence, based on the explanatory relationship between evidence and conclusion. Drawing on the literature on graphical models of explanation, I divide inductive inferences into (a) downwards inferences, which proceed from cause to effect, (b) upwards inferences, which proceed from effect to cause, and (c) sideways inferences, which proceed first from effect to cause and then from that cause to an additional effect. I further (...)
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  • Admissibility Troubles for Bayesian Direct Inference Principles.Christian Wallmann & James Hawthorne - 2020 - Erkenntnis 85 (4):957-993.
    Direct inferences identify certain probabilistic credences or confirmation-function-likelihoods with values of objective chances or relative frequencies. The best known version of a direct inference principle is David Lewis’s Principal Principle. Certain kinds of statements undermine direct inferences. Lewis calls such statements inadmissible. We show that on any Bayesian account of direct inference several kinds of intuitively innocent statements turn out to be inadmissible. This may pose a significant challenge to Bayesian accounts of direct inference. We suggest some ways in which (...)
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  • Agreeing to disagree and dilation.Jiji Zhang, Hailin Liu & Teddy Seidenfeld - unknown
    We consider Geanakoplos and Polemarchakis’s generalization of Aumman’s famous result on “agreeing to disagree", in the context of imprecise probability. The main purpose is to reveal a connection between the possibility of agreeing to disagree and the interesting and anomalous phenomenon known as dilation. We show that for two agents who share the same set of priors and update by conditioning on every prior, it is impossible to agree to disagree on the lower or upper probability of a hypothesis unless (...)
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  • Why indeterminate probability is rational.Isaac Levi - 2009 - Journal of Applied Logic 7 (4):364-376.
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  • Conditional Degree of Belief and Bayesian Inference.Jan Sprenger - 2020 - Philosophy of Science 87 (2):319-335.
    Why are conditional degrees of belief in an observation E, given a statistical hypothesis H, aligned with the objective probabilities expressed by H? After showing that standard replies are not satisfactory, I develop a suppositional analysis of conditional degree of belief, transferring Ramsey’s classical proposal to statistical inference. The analysis saves the alignment, explains the role of chance-credence coordination, and rebuts the charge of arbitrary assessment of evidence in Bayesian inference. Finally, I explore the implications of this analysis for Bayesian (...)
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  • Deference and description.Aaron Bronfman - 2015 - Philosophical Studies 172 (5):1333-1353.
    Consider someone whom you know to be an expert about some issue. She knows at least as much as you do and reasons impeccably. The issue is a straightforward case of statistical inference that raises no deep problems of epistemology. You happen to know the expert’s opinion on this issue. Should you defer to her by adopting her opinion as your own? An affirmative answer may appear mandatory. But this paper argues that a crucial factor in answering this question is (...)
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  • Equivocation for the Objective Bayesian.George Masterton - 2015 - Erkenntnis 80 (2):403-432.
    According to Williamson , the difference between empirical subjective Bayesians and objective Bayesians is that, while both hold reasonable credence to be calibrated to evidence, the objectivist also takes such credence to be as equivocal as such calibration allows. However, Williamson’s prescription for equivocation generates constraints on reasonable credence that are objectionable. Herein Williamson’s calibration norm is explicated in a novel way that permits an alternative equivocation norm. On this alternative account, evidence calibrated probability functions are recognised as implications of (...)
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  • Conditional probability, taxicabs, and martingales.Brian Skyrms - 1981 - Behavioral and Brain Sciences 4 (3):351-352.
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  • Cohen on contraposition.N. E. Wetherick - 1981 - Behavioral and Brain Sciences 4 (3):358-358.
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  • Human reasoning: Can we judge before we understand?Richard A. Griggs - 1981 - Behavioral and Brain Sciences 4 (3):338-339.
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  • Independent forebrain and brainstem controls for arousal and sleep.Jaime R. Villablanca - 1981 - Behavioral and Brain Sciences 4 (3):494-496.
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  • Can any statements about human behavior be empirically validated?Baruch Fischoff - 1981 - Behavioral and Brain Sciences 4 (3):336-337.
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  • L. J. Cohen versus Bayesianism.Ilkka Niiniluoto - 1981 - Behavioral and Brain Sciences 4 (3):349-349.
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  • Demystifying Dilation.Arthur Paul Pedersen & Gregory Wheeler - 2014 - Erkenntnis 79 (6):1305-1342.
    Dilation occurs when an interval probability estimate of some event E is properly included in the interval probability estimate of E conditional on every event F of some partition, which means that one’s initial estimate of E becomes less precise no matter how an experiment turns out. Critics maintain that dilation is a pathological feature of imprecise probability models, while others have thought the problem is with Bayesian updating. However, two points are often overlooked: (1) knowing that E is stochastically (...)
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  • Causal Probability.John L. John L. - 2002 - Synthese 132 (1/2):143-185.
    Examples growing out of the Newcomb problem have convinced many people that decision theory should proceed in terms of some kind of causal probability. I endorse this view and define and investigate a variety of causal probability. My definition is related to Skyrms' definition, but proceeds in terms of objective probabilities rather than subjective probabilities and avoids taking causal dependence as a primitive concept.
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  • Two Problems of Direct Inference.Paul D. Thorn - 2012 - Erkenntnis 76 (3):299-318.
    The article begins by describing two longstanding problems associated with direct inference. One problem concerns the role of uninformative frequency statements in inferring probabilities by direct inference. A second problem concerns the role of frequency statements with gerrymandered reference classes. I show that past approaches to the problem associated with uninformative frequency statements yield the wrong conclusions in some cases. I propose a modification of Kyburg’s approach to the problem that yields the right conclusions. Past theories of direct inference have (...)
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  • Multiple Studies and Evidential Defeat.Matthew Kotzen - 2011 - Noûs 47 (1):154-180.
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  • Probabilistic Logics and Probabilistic Networks.Rolf Haenni, Jan-Willem Romeijn, Gregory Wheeler & Jon Williamson - 2010 - Dordrecht, Netherland: Synthese Library. Edited by Gregory Wheeler, Rolf Haenni, Jan-Willem Romeijn & and Jon Williamson.
    Additionally, the text shows how to develop computationally feasible methods to mesh with this framework.
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  • What's in a numeral?David Miller - 1979 - Philosophical Studies 35 (4):323 - 344.
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  • Subjunctives, dispositions and chances.Isaac Levi - 1977 - Synthese 34 (4):423 - 455.
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  • Conditionals and consequences.Gregory Wheeler, Henry E. Kyburg & Choh Man Teng - 2007 - Journal of Applied Logic 5 (4):638-650.
    We examine the notion of conditionals and the role of conditionals in inductive logics and arguments. We identify three mistakes commonly made in the study of, or motivation for, non-classical logics. A nonmonotonic consequence relation based on evidential probability is formulated. With respect to this acceptance relation some rules of inference of System P are unsound, and we propose refinements that hold in our framework.
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  • (1 other version)Dissonance and Consistency according to Shackle and Shafer.Isaac Levi - 1978 - PSA Proceedings of the Biennial Meeting of the Philosophy of Science Association 1978 (2):466-477.
    R.A.Fisher introduced the fiducial argument as a means for obtaining something from nothing. He thought that on some occasions it was legitimate to obtain a posterior probability distribution over a range of simple statistical hypotheses without commitment to a prior distribution [4].H.Jeffreys thought he could tame Fisher by casting his argument in a Bayesian mold through a derivation of the fiducial posterior from a suitably constructed ignorance prior via Bayes’ theorem and conditionalization on the data of experimentation. According to Jeffreys, (...)
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  • (1 other version)An Argument for the Principle of Indifference and Against the Wide Interval View.John E. Wilcox - 2020 - Journal for General Philosophy of Science / Zeitschrift für Allgemeine Wissenschaftstheorie 51 (1):65-87.
    The principle of indifference has fallen from grace in contemporary philosophy, yet some papers have recently sought to vindicate its plausibility. This paper follows suit. In it, I articulate a version of the principle and provide what appears to be a novel argument in favour of it. The argument relies on a thought experiment where, intuitively, an agent’s confidence in any particular outcome being true should decrease with the addition of outcomes to the relevant space of possible outcomes. Put simply: (...)
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  • The ubiquitous defeaters: no admissibility troubles for Bayesian accounts of direct inference.Zalán Gyenis & Leszek Wronski - unknown
    In this paper we dispel the supposed ``admissibility troubles'' for Bayesian accounts of direct inference proposed by Wallmann and Hawthorne, which concern the existence of surprising, unintuitive defeaters even for mundane cases of direct inference. We show that if one follows the majority of authors in the field in using classical probability spaces unimbued with any additional structure, one should expect similar phenomena to arise and should consider them unproblematic in themselves: defeaters abound! We then show that the framework of (...)
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  • Machine Epistemology and Big Data.Gregory Wheeler - 2016 - In Lee C. McIntyre & Alexander Rosenberg (eds.), The Routledge Companion to Philosophy of Social Science. New York: Routledge.
    In the age of big data and a machine epistemology that can anticipate, predict, and intervene on events in our lives, the problem once again is that a few individuals possess the knowledge of how to regulate these activities. But the question we face now is not how to share such knowledge more widely, but rather of how to enjoy the public benefits bestowed by this knowledge without freely sharing it. It is not merely personal privacy that is at stake (...)
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  • Rational animal?Simon Blackburn - 1981 - Behavioral and Brain Sciences 4 (3):331-332.
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  • Can children's irrationality be experimentally demonstrated?Sam Glucksberg - 1981 - Behavioral and Brain Sciences 4 (3):337-338.
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  • The irrational, the unreasonable, and the wrong.Avishai Margalit & Maya Bar-Hillel - 1981 - Behavioral and Brain Sciences 4 (3):346-349.
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  • Another vote for rationality.Mary Henle - 1981 - Behavioral and Brain Sciences 4 (3):339-339.
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  • Who shall be the arbiter of our intuitions?Daniel Kahneman - 1981 - Behavioral and Brain Sciences 4 (3):339-340.
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  • Some questions regarding the rationality of a demonstration of human rationality.Robert J. Sternberg - 1981 - Behavioral and Brain Sciences 4 (3):352-353.
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  • A theory of direct inference.John L. Pollock - 1983 - Theory and Decision 15 (1):29-95.
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  • The Metaphysics of Chance.Rachael Briggs - 2010 - Philosophy Compass 5 (11):938-952.
    This article surveys several interrelated issues in the metaphysics of chance. First, what is the relationship between the probabilities associated with types of trials (for instance, the chance that a twenty‐eight‐year old develops diabetes before age thirty) and the probabilities associated with individual token trials (for instance, the chance that I develop diabetes before age thirty)? Second, which features of the the world fix the chances: are there objective chances at all, and if so, are there non‐chancy facts on which (...)
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  • Causal probability.John L. Pollock - 2002 - Synthese 132 (1-2):143 - 185.
    Examples growing out of the Newcomb problem have convinced many people that decision theory should proceed in terms of some kind of causal probability. I endorse this view and define and investigate a variety of causal probability. My definition is related to Skyrms' definition, but proceeds in terms of objective probabilities rather than subjective probabilities and avoids taking causal dependence as a primitive concept.
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  • Should Bayesians sometimes neglect base rates?Isaac Levi - 1981 - Behavioral and Brain Sciences 4 (3):342-343.
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  • “Is” and “ought” in cognitive science.William G. Lycan - 1981 - Behavioral and Brain Sciences 4 (3):344-345.
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  • Lay arbitration of rules of inference.Richard E. Nisbett - 1981 - Behavioral and Brain Sciences 4 (3):349-350.
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  • Human rationality: Misleading linguistic analogies.Geoffrey Sampson - 1981 - Behavioral and Brain Sciences 4 (3):350-351.
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  • Rationality and the sanctity of competence.Hillel J. Einhorn & Robin M. Hogarth - 1981 - Behavioral and Brain Sciences 4 (3):334-335.
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  • The importance of cognitive illusions.Peter Wason - 1981 - Behavioral and Brain Sciences 4 (3):356-356.
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  • A note on Pollock's system of direct inference.Stephen Leeds - 1994 - Theory and Decision 36 (3):247-256.
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