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33 found
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  1. How to Analyse Retrodictive Probabilities in Inference to the Best Explanation.Andrew Holster - manuscript
    IBE ('Inference to the best explanation' or abduction) is a popular and highly plausible theory of how we should judge the evidence for claims of past events based on present evidence. It has been notably developed and supported recently by Meyer following Lipton. I believe this theory is essentially correct. This paper supports IBE from a probability perspective, and argues that the retrodictive probabilities involved in such inferences should be analysed in terms of predictive probabilities and a priori probability ratios (...)
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  2. More trouble for regular probabilitites.Matthew W. Parker - 2012
    In standard probability theory, probability zero is not the same as impossibility. But many have suggested that only impossible events should have probability zero. This can be arranged if we allow infinitesimal probabilities, but infinitesimals do not solve all of the problems. We will see that regular probabilities are not invariant over rigid transformations, even for simple, bounded, countable, constructive, and disjoint sets. Hence, regular chances cannot be determined by space-time invariant physical laws, and regular credences cannot satisfy seemingly reasonable (...)
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  3. Uniqueness and Modesty: How Permissivists Can Live on the Edge.Darren Bradley - forthcoming - Mind.
    There is a divide in epistemology between those who think that, for any hypothesis and set of total evidence, there is a unique rational credence in that hypothesis, and those who think that there can be many rational credences. Schultheis offers a novel and potentially devastating objection to Permissivism, on the grounds that Permissivism permits dominated credences. I will argue that Permissivists can plausibly block Schultheis' argument. The issue turns on getting clear about whether we should be certain whether our (...)
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  4. Better Foundations for Subjective Probability.Sven Neth - forthcoming - Australasian Journal of Philosophy.
    How do we ascribe subjective probability? In decision theory, this question is often addressed by representation theorems, going back to Ramsey (1926), which tell us how to define or measure subjective probability by observable preferences. However, standard representation theorems make strong rationality assumptions, in particular expected utility maximization. How do we ascribe subjective probability to agents which do not satisfy these strong rationality assumptions? I present a representation theorem with weak rationality assumptions which can be used to define or measure (...)
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  5. Epistemic Probabilities are Degrees of Support, not Degrees of (Rational) Belief.Nevin Climenhaga - 2024 - Philosophy and Phenomenological Research 108 (1):153-176.
    I argue that when we use ‘probability’ language in epistemic contexts—e.g., when we ask how probable some hypothesis is, given the evidence available to us—we are talking about degrees of support, rather than degrees of belief. The epistemic probability of A given B is the mind-independent degree to which B supports A, not the degree to which someone with B as their evidence believes A, or the degree to which someone would or should believe A if they had B as (...)
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  6. 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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  7. A Dilemma for Solomonoff Prediction.Sven Neth - 2023 - Philosophy of Science 90 (2):288-306.
    The framework of Solomonoff prediction assigns prior probability to hypotheses inversely proportional to their Kolmogorov complexity. There are two well-known problems. First, the Solomonoff prior is relative to a choice of Universal Turing machine. Second, the Solomonoff prior is not computable. However, there are responses to both problems. Different Solomonoff priors converge with more and more data. Further, there are computable approximations to the Solomonoff prior. I argue that there is a tension between these two responses. This is because computable (...)
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  8. Absence of evidence against belief as credence 1.Andrew del Rio - 2022 - Analysis 83 (1):31-39.
    On one view of the traditional doxastic attitudes, belief is credence 1, disbelief is credence 0 and suspension is any precise credence between 0 and 1. In ‘Rational agnosticism and degrees of belief’ (2013) Jane Friedman argues, against this view, that there are cases where a credence of 0 is required but where suspension is permitted. If this were so, belief, disbelief and suspension could not be identified or reduced to the aforementioned credences. I argue that Friedman relies on two (...)
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  9. Multiple Universes and Self-Locating Evidence.Yoaav Isaacs, John Hawthorne & Jeffrey Sanford Russell - 2022 - Philosophical Review 131 (3):241-294.
    Is the fact that our universe contains fine-tuned life evidence that we live in a multiverse? Ian Hacking and Roger White influentially argue that it is not. We approach this question through a systematic framework for self-locating epistemology. As it turns out, leading approaches to self-locating evidence agree that the fact that our own universe contains fine-tuned life indeed confirms the existence of a multiverse. This convergence is no accident: we present two theorems showing that, in this setting, any updating (...)
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  10. A preamble about doing research that sells.Quan-Hoang Vuong - 2022 - In Quan-Hoang Vuong, Minh-Hoang Nguyen & Viet-Phuong La (eds.), The mindsponge and BMF analytics for innovative thinking in social sciences and humanities. Berlin, Germany: De Gruyter.
    Being a researcher is challenging, especially in the beginning. Early Career Researchers (ECRs) need achievements to secure and expand their careers. In today’s academic landscape, researchers are under many pressures: data collection costs, the expectation of novelty, analytical skill requirements, lengthy publishing process, and the overall competitiveness of the career. Innovative thinking and the ability to turn good ideas into good papers are the keys to success.
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  11. (1 other version)Objective Bayesianism and the Abductivist Response to Scepticism.Darren Bradley - 2021 - Episteme 1:1-15.
    An important line of response to scepticism appeals to the best explanation. But anti-sceptics have not engaged much with work on explanation in the philosophy of science. I plan to investigate whether plausible assumptions about best explanations really do favour anti-scepticism. I will argue that there are ways of constructing sceptical hypotheses in which the assumptions do favour anti-scepticism, but the size of the support for anti-scepticism is small.
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  12. Conglomerability, disintegrability and the comparative principle.Rush T. Stewart & Michael Nielsen - 2021 - Analysis 81 (3):479-488.
    Our aim here is to present a result that connects some approaches to justifying countable additivity. This result allows us to better understand the force of a recent argument for countable additivity due to Easwaran. We have two main points. First, Easwaran’s argument in favour of countable additivity should have little persuasive force on those permissive probabilists who have already made their peace with violations of conglomerability. As our result shows, Easwaran’s main premiss – the comparative principle – is strictly (...)
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  13. Naturalness as a Constraint on Priors.Darren Bradley - 2020 - Mind 129 (513):179-203.
    Many epistemological problems can be solved by the objective Bayesian view that there are rationality constraints on priors, that is, inductive probabilities. But attempts to work out these constraints have run into such serious problems that many have rejected objective Bayesianism altogether. I argue that the epistemologist should borrow the metaphysician’s concept of naturalness and assign higher priors to more natural hypotheses.
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  14. The structure of epistemic probabilities.Nevin Climenhaga - 2020 - Philosophical Studies 177 (11):3213-3242.
    The epistemic probability of A given B is the degree to which B evidentially supports A, or makes A plausible. This paper is a first step in answering the question of what determines the values of epistemic probabilities. I break this question into two parts: the structural question and the substantive question. Just as an object’s weight is determined by its mass and gravitational acceleration, some probabilities are determined by other, more basic ones. The structural question asks what probabilities are (...)
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  15. Bayesian Beauty.Silvia Milano - 2020 - Erkenntnis 87 (2):657-676.
    The Sleeping Beauty problem has attracted considerable attention in the literature as a paradigmatic example of how self-locating uncertainty creates problems for the Bayesian principles of Conditionalization and Reflection. Furthermore, it is also thought to raise serious issues for diachronic Dutch Book arguments. I show that, contrary to what is commonly accepted, it is possible to represent the Sleeping Beauty problem within a standard Bayesian framework. Once the problem is correctly represented, the ‘thirder’ solution satisfies standard rationality principles, vindicating why (...)
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  16. On the Probability of Plenitude.Jeffrey Sanford Russell - 2020 - Journal of Philosophy 117 (5):267-292.
    I examine what the mathematical theory of random structures can teach us about the probability of Plenitude, a thesis closely related to David Lewis's modal realism. Given some natural assumptions, Plenitude is reasonably probable a priori, but in principle it can be (and plausibly it has been) empirically disconfirmed—not by any general qualitative evidence, but rather by our de re evidence.
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  17. Trung tâm ISR có bài ra mừng 130 năm Ngày sinh Chủ tịch Hồ Chí Minh.Hồ Mạnh Toàn - 2020 - ISR Phenikaa 2020 (5):1-3.
    Bài mới xuất bản vào ngày 19-5-2020 với tác giả liên lạc là NCS Nguyễn Minh Hoàng, cán bộ nghiên cứu của Trung tâm ISR, trình bày tiếp cận thống kê Bayesian cho việc nghiên cứu dữ liệu khoa học xã hội. Đây là kết quả của định hướng Nhóm nghiên cứu SDAG được nêu rõ ngay từ ngày 18-5-2019.
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  18. Are non-accidental regularities a cosmic coincidence? Revisiting a central threat to Humean laws.Aldo Filomeno - 2019 - Synthese 198 (6):5205-5227.
    If the laws of nature are as the Humean believes, it is an unexplained cosmic coincidence that the actual Humean mosaic is as extremely regular as it is. This is a strong and well-known objection to the Humean account of laws. Yet, as reasonable as this objection may seem, it is nowadays sometimes dismissed. The reason: its unjustified implicit assignment of equiprobability to each possible Humean mosaic; that is, its assumption of the principle of indifference, which has been attacked on (...)
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  19. Carnap’s epistemological critique of metaphysics.Darren Bradley - 2018 - Synthese 195 (5):2247-2265.
    Many who take a dismissive attitude towards metaphysics trace their view back to Carnap’s ‘Empiricism, Semantics and Ontology’. But the reason Carnap takes a dismissive attitude to metaphysics is a matter of controversy. I will argue that no reason is given in ‘Empiricism, Semantics and Ontology’, and this is because his reason for rejecting metaphysical debates was given in ‘Pseudo-Problems in Philosophy’. The argument there assumes verificationism, but I will argue that his argument survives the rejection of verificationism. The root (...)
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  20. R Code for Preliminary Checks (Bayesian Method).Quan-Hoang Vuong - 2018 - AISDL 2018 (1):1-5.
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  21. Self-locating Priors and Cosmological Measures.Frank Arntzenius & Cian Dorr - 2017 - In Khalil Chamcham, John Barrow, Simon Saunders & Joe Silk (eds.), The Philosophy of Cosmology. Cambridge, United Kingdom: Cambridge University Press. pp. 396-428.
    We develop a Bayesian framework for thinking about the way evidence about the here and now can bear on hypotheses about the qualitative character of the world as a whole, including hypotheses according to which the total population of the world is infinite. We show how this framework makes sense of the practice cosmologists have recently adopted in their reasoning about such hypotheses.
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  22. 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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  23. The Principle of Indifference and Inductive Scepticism.Robert Smithson - 2017 - British Journal for the Philosophy of Science 68 (1):253-272.
    Many theorists have proposed that we can use the principle of indifference to defeat the inductive sceptic. But any such theorist must confront the objection that different ways of applying the principle of indifference lead to incompatible probability assignments. Huemer offers the explanatory priority proviso as a strategy for overcoming this objection. With this proposal, Huemer claims that we can defend induction in a way that is not question-begging against the sceptic. But in this article, I argue that the opposite (...)
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  24. Ur-Priors, Conditionalization, and Ur-Prior Conditionalization.Christopher J. G. Meacham - 2016 - Ergo: An Open Access Journal of Philosophy 3.
    Conditionalization is a widely endorsed rule for updating one’s beliefs. But a sea of complaints have been raised about it, including worries regarding how the rule handles error correction, changing desiderata of theory choice, evidence loss, self-locating beliefs, learning about new theories, and confirmation. In light of such worries, a number of authors have suggested replacing Conditionalization with a different rule — one that appeals to what I’ll call “ur-priors”. But different authors have understood the rule in different ways, and (...)
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  25. Scepticism, defeasible evidence and entitlement.Daniele Sgaravatti - 2014 - Philosophical Studies 168 (2):439-455.
    The paper starts by describing and clarifying what Williamson calls the consequence fallacy. I show two ways in which one might commit the fallacy. The first, which is rather trivial, involves overlooking background information; the second way, which is the more philosophically interesting, involves overlooking prior probabilities. In the following section, I describe a powerful form of sceptical argument, which is the main topic of the paper, elaborating on previous work by Huemer. The argument attempts to show the impossibility of (...)
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  26. (1 other version)Upping the Stakes and the Preface Paradox.Jonny Blamey - 2013 - In Frank Zenker (ed.), Bayesian Argumentation. Springer. pp. 195-210.
    Abstract The Preface Paradox, first introduced by David Makinson (1961), presents a plausible scenario where an agent is evidentially certain of each of a set of propositions without being evidentially certain of the conjunction of the set of propositions. Given reasonable assumptions about the nature of evidential certainty, this appears to be a straightforward contradiction. We solve the paradox by appeal to stake size sensitivity, which is the claim that evidential probability is sensitive to stake size. The argument is that (...)
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  27. Reasons for (prior) belief in Bayesian epistemology.Franz Dietrich & Christian List - 2013 - Synthese 190 (5):781-786.
    Bayesian epistemology tells us with great precision how we should move from prior to posterior beliefs in light of new evidence or information, but says little about where our prior beliefs come from. It offers few resources to describe some prior beliefs as rational or well-justified, and others as irrational or unreasonable. A different strand of epistemology takes the central epistemological question to be not how to change one’s beliefs in light of new evidence, but what reasons justify a given (...)
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  28. Impermissive Bayesianism.Christopher J. G. Meacham - 2013 - Erkenntnis 79 (Suppl 6):1185-1217.
    This paper examines the debate between permissive and impermissive forms of Bayesianism. It briefly discusses some considerations that might be offered by both sides of the debate, and then replies to some new arguments in favor of impermissivism offered by Roger White. First, it argues that White’s (Oxford studies in epistemology, vol 3. Oxford University Press, Oxford, pp 161–186, 2010) defense of Indifference Principles is unsuccessful. Second, it contends that White’s (Philos Perspect 19:445–459, 2005) arguments against permissive views do not (...)
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  29. Regularity reformulated.Weng Hong Tang - 2012 - Episteme 9 (4):329-343.
    This paper focuses on the view that rationality requires that our credences be regular. I go through different formulations of the requirement, and show that they face several problems. I then formulate a version of the requirement that solves most of, if not all, these problems. I conclude by showing that an argument thought to support the requirement as traditionally formulated actually does not; if anything, the argument, slightly modified, supports my version of the requirement.Send article to KindleTo send this (...)
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  30. Reasonable doubt : uncertainty in education, science and law.Tony Gardner-Medwin - 2011 - In Philip Dawid, William Twining & Mimi Vasilaki (eds.), Evidence, Inference and Enquiry. Oxford: Oup/British Academy. pp. 465-483.
    The use of evidence to resolve uncertainties is key to many endeavours, most conspicuously science and law. Despite this, the logic of uncertainty is seldom taught explicitly, and often seems misunderstood. Traditional educational practice even fails to encourage students to identify uncertainty when they express knowledge, though mark schemes that reward the identification of reliable and uncertain responses have long been shown to encourage more insightful understanding. In our information-rich society the ability to identify uncertainty is often more important than (...)
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  31. Wittgenstein on Prior Probabilities.Michael E. Cuffaro - 2010 - Proceedings of the Canadian Society for History and Philosophy of Mathematics 23:85-98.
    Wittgenstein did not write very much on the topic of probability. The little we have comes from a few short pages of the Tractatus, some 'remarks' from the 1930s, and the informal conversations which went on during that decade with the Vienna Circle. Nevertheless, Wittgenstein's views were highly influential in the later development of the logical theory of probability. This paper will attempt to clarify and defend Wittgenstein's conception of probability against some oft-cited criticisms that stem from a misunderstanding of (...)
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  32. Sleeping beauty and the dynamics of de se beliefs.Christopher J. G. Meacham - 2008 - Philosophical Studies 138 (2):245-269.
    This paper examines three accounts of the sleeping beauty case: an account proposed by Adam Elga, an account proposed by David Lewis, and a third account defended in this paper. It provides two reasons for preferring the third account. First, this account does a good job of capturing the temporal continuity of our beliefs, while the accounts favored by Elga and Lewis do not. Second, Elga’s and Lewis’ treatments of the sleeping beauty case lead to highly counterintuitive consequences. The proposed (...)
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  33. The Bayesian and the Dogmatist.Brian Weatherson - 2007 - Proceedings of the Aristotelian Society 107 (1pt2):169-185.
    It has been argued recently that dogmatism in epistemology is incompatible with Bayesianism. That is, it has been argued that dogmatism cannot be modelled using traditional techniques for Bayesian modelling. I argue that our response to this should not be to throw out dogmatism, but to develop better modelling techniques. I sketch a model for formal learning in which an agent can discover a posteriori fundamental epistemic connections. In this model, there is no formal objection to dogmatism.
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