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  1. Probability Out Of Determinism.Michael Strevens - 2011 - In Claus Beisbart & Stephan Hartmann (eds.), Probabilities in Physics. Oxford University Press. pp. 339--364.
    This paper offers a metaphysics of physical probability in (or if you prefer, truth conditions for probabilistic claims about) deterministic systems based on an approach to the explanation of probabilistic patterns in deterministic systems called the method of arbitrary functions. Much of the appeal of the method is its promise to provide an account of physical probability on which probability assignments have the ability to support counterfactuals about frequencies. It is argued that the eponymous arbitrary functions are of little philosophical (...)
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  2. The Concept of Probability in Physics: An Analytic Version of von Mises’ Interpretation.Louis Vervoort - manuscript
    In the following we will investigate whether von Mises’ frequency interpretation of probability can be modified to make it philosophically acceptable. We will reject certain elements of von Mises’ theory, but retain others. In the interpretation we propose we do not use von Mises’ often criticized ‘infinite collectives’ but we retain two essential claims of his interpretation, stating that probability can only be defined for events that can be repeated in similar conditions, and that exhibit frequency stabilization. The central idea (...)
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  3. Innate Right in Kant—A Critical Reading.Katrin Flikschuh - forthcoming - Wiley: European Journal of Philosophy.
    European Journal of Philosophy, EarlyView.
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  4. Powers, Probabilities, and Tendencies.Michele Paolini Paoletti - 2022 - Philosophia:1-33.
    In this article, I aim at showing how powers may ground different types of probability in the universe. In Section 1 I single out several dimensions along which the probability of something can be determined. Each of such dimensions can be further specified at the type-level or at the token-level. In Section 2 I introduce some metaphysical assumptions about powers. In Section 3 I show how powers can ground single-case probabilities and frequency-probabilities in a deterministic setting. Later on, in Section (...)
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  5. Machine-Believers Learning Faiths & Knowledges: The New Gospel of Artificial Intelligence.Virgil W. Brower - 2021 - Internationales Jahrbuch Für Medienphilosophie 7 (1):97-121.
    One is occasionally reminded of Foucault's proclamation in a 1970 interview that "perhaps, one day this century will be known as Deleuzian." Less often is one compelled to update and restart with a supplementary counter-proclamation of the mathematician, David Lindley: "the twenty-first century would be a Bayesian era..." The verb tenses of both are conspicuous. // To critically attend to what is today often feared and demonized, but also revered, deployed, and commonly referred to as algorithm(s), one cannot avoid the (...)
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  6. Westphal, Kenneth, Kant’s Critical Epistemology: Why Epistemology Must Consider Judgment First. [REVIEW]Ekin Erkan - 2021 - Argumenta 12:366-373.
  7. Reviving Frequentism.Mario Hubert - 2021 - Synthese 199:5255–5584.
    Philosophers now seem to agree that frequentism is an untenable strategy to explain the meaning of probabilities. Nevertheless, I want to revive frequentism, and I will do so by grounding probabilities on typicality in the same way as the thermodynamic arrow of time can be grounded on typicality within statistical mechanics. This account, which I will call typicality frequentism, will evade the major criticisms raised against previous forms of frequentism. In this theory, probabilities arise within a physical theory from statistical (...)
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  8. Laura Papish, Kant on Evil, Self-Deception, and Moral Reform. [REVIEW]Samuel Kahn - 2021 - Ethics 132 (1):266-269.
    Laura Papish’s Kant on Evil, Self-Deception, and Moral Reform is an ambitious attempt to breath new life into old debates and a welcome contribution to a recent renaissance of interest in Kant’s theory of evil. ​The book has eight chapters, and these chapters fall into three main divisions. Chapters 1 and 2 focus on the psychology of nonmoral and immoral action. Chapters 3, 4, and 5 focus on self-deception, evil, and dissimulation. And chapters 6, 7, and 8 focus on self-cognition, (...)
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  9. A Battle in the Statistics Wars: A Simulation-Based Comparison of Bayesian, Frequentist and Williamsonian Methodologies.Mantas Radzvilas, William Peden & Francesco De Pretis - 2021 - Synthese 199 (5-6):13689-13748.
    The debates between Bayesian, frequentist, and other methodologies of statistics have tended to focus on conceptual justifications, sociological arguments, or mathematical proofs of their long run properties. Both Bayesian statistics and frequentist (“classical”) statistics have strong cases on these grounds. In this article, we instead approach the debates in the “Statistics Wars” from a largely unexplored angle: simulations of different methodologies’ performance in the short to medium run. We conducted a large number of simulations using a straightforward decision problem based (...)
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  10. What Type of Type I Error? Contrasting the Neyman–Pearson and Fisherian Approaches in the Context of Exact and Direct Replications.Mark Rubin - 2021 - Synthese 198 (6):5809–5834.
    The replication crisis has caused researchers to distinguish between exact replications, which duplicate all aspects of a study that could potentially affect the results, and direct replications, which duplicate only those aspects of the study that are thought to be theoretically essential to reproduce the original effect. The replication crisis has also prompted researchers to think more carefully about the possibility of making Type I errors when rejecting null hypotheses. In this context, the present article considers the utility of two (...)
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  11. “Repeated Sampling From the Same Population?” A Critique of Neyman and Pearson’s Responses to Fisher.Mark Rubin - 2020 - European Journal for Philosophy of Science 10 (3):1-15.
    Fisher criticised the Neyman-Pearson approach to hypothesis testing by arguing that it relies on the assumption of “repeated sampling from the same population.” The present article considers the responses to this criticism provided by Pearson and Neyman. Pearson interpreted alpha levels in relation to imaginary replications of the original test. This interpretation is appropriate when test users are sure that their replications will be equivalent to one another. However, by definition, scientific researchers do not possess sufficient knowledge about the relevant (...)
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  12. No Interpretation of Probability.Wolfgang Schwarz - 2018 - Erkenntnis 83 (6):1195-1212.
    I argue that none of the usual interpretations of probability provide an adequate interpretation of probabilistic theories in science. Assuming that the aim of such theories is to capture noisy relationships in the world, I suggest that we do not have to give them classical truth-conditional content at all: their probabilities can remain uninterpreted. Indirectly, this account turns out to explain what is right about the frequency interpretation, the best-systems interpretation, and the epistemic interpretation.
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  13. Phylogenetic Inference, Selection Theory, and History of Science: Selected Papers of A. W. F. Edwards with Commentaries.Rasmus Grønfeldt Winther - 2018 - Cambridge: Cambridge University Press.
    A. W. F. Edwards is one of the most influential mathematical geneticists in the history of the discipline. One of the last students of R. A. Fisher, Edwards pioneered the statistical analysis of phylogeny in collaboration with L. L. Cavalli-Sforza, and helped establish Fisher's concept of likelihood as a standard of statistical and scientific inference. In this book, edited by philosopher of science Rasmus Grønfeldt Winther, Edwards's key papers are assembled alongside commentaries by leading scientists, discussing Edwards's influence on their (...)
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  14. 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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  15. Pragmatic Warrant for Frequentist Statistical Practice: The Case of High Energy Physics.Kent Staley - 2017 - Synthese 194 (2).
    Amidst long-running debates within the field, high energy physics has adopted a statistical methodology that primarily employs standard frequentist techniques such as significance testing and confidence interval estimation, but incorporates Bayesian methods for limited purposes. The discovery of the Higgs boson has drawn increased attention to the statistical methods employed within HEP. Here I argue that the warrant for the practice in HEP of relying primarily on frequentist methods can best be understood as pragmatic, in the sense that statistical methods (...)
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  16. Coping with Ethical Uncertainty.John R. Welch - 2017 - Diametros 53:150-166.
    Most ethical decisions are conditioned by formidable uncertainty. Decision makers may lack reliable information about relevant facts, the consequences of actions, and the reactions of other people. Resources for dealing with uncertainty are available from standard forms of decision theory, but successful application to decisions under risk requires a great deal of quantitative information: point-valued probabilities of states and point-valued utilities of outcomes. When this information is not available, this paper recommends the use of a form of decision theory that (...)
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  17. Probability and Randomness.Antony Eagle - 2016 - In Alan Hájek & Christopher Hitchcock (eds.), The Oxford Handbook of Probability and Philosophy. Oxford, U.K.: Oxford University Press. pp. 440-459.
    Early work on the frequency theory of probability made extensive use of the notion of randomness, conceived of as a property possessed by disorderly collections of outcomes. Growing out of this work, a rich mathematical literature on algorithmic randomness and Kolmogorov complexity developed through the twentieth century, but largely lost contact with the philosophical literature on physical probability. The present chapter begins with a clarification of the notions of randomness and probability, conceiving of the former as a property of a (...)
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  18. The Oxford Handbook of Probability and Philosophy.Alan Hajek & Christopher Hitchcock (eds.) - 2016 - Oxford: Oxford University Press.
    Probability theory is a key tool of the physical, mathematical, and social sciences. It has also been playing an increasingly significant role in philosophy: in epistemology, philosophy of science, ethics, social philosophy, philosophy of religion, and elsewhere. This Handbook encapsulates and furthers the influence of philosophy on probability, and of probability on philosophy. Nearly forty articles summarise the state of play and present new insights in various areas of research at the intersection of these two fields. The articles will be (...)
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  19. On the Correct Interpretation of P Values and the Importance of Random Variables.Guillaume Rochefort-Maranda - 2016 - Synthese 193 (6):1777-1793.
    The p value is the probability under the null hypothesis of obtaining an experimental result that is at least as extreme as the one that we have actually obtained. That probability plays a crucial role in frequentist statistical inferences. But if we take the word ‘extreme’ to mean ‘improbable’, then we can show that this type of inference can be very problematic. In this paper, I argue that it is a mistake to make such an interpretation. Under minimal assumptions about (...)
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  20. Finite Frequentism in a Big World.Nick Tosh - 2016 - British Journal for the Philosophy of Science 67 (1):169-213.
    The view that chances are relative frequencies of occurrence within actual, finite reference classes has long been written off. I argue that it ought to be reconsidered. Focusing on non-deterministic chance, I defend a version of finite frequentism in which reference classmates are required to have qualitatively identical pasts. While my analysis can evade or resist several standard objections, it has a counterintuitive consequence: non-trivial chances entail the existence of past light cones that are perfect intrinsic duplicates. In mitigation, I (...)
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  21. Counting Steps: A Finitist Interpretation of Objective Probability in Physics.Amit Hagar & Giuseppe Sergioli - 2015 - Epistemologia 37 (2):262-275.
    We propose a new interpretation of objective deterministic chances in statistical physics based on physical computational complexity. This notion applies to a single physical system (be it an experimental set--up in the lab, or a subsystem of the universe), and quantifies (1) the difficulty to realize a physical state given another, (2) the 'distance' (in terms of physical resources) from a physical state to another, and (3) the size of the set of time--complexity functions that are compatible with the physical (...)
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  22. Good Just Isn't Good Enough - Humean Chances and Boltzmannian Statistical Physics.Claus Beisbart - 2014 - In Maria C. Galavotti (ed.), New Directions in the Philosophy of Science, The Philosophy of Science in a European Perspective 5. Springer. pp. 511-529.
    Statistical physicists assume a probability distribution over micro-states to explain thermodynamic behavior. The question of this paper is whether these probabilities are part of a best system and can thus be interpreted as Humean chances. I consider two Boltzmannian accounts of the Second Law, viz. a globalist and a localist one. In both cases, the probabilities fail to be chances because they have rivals that are roughly equally good. I conclude with the diagnosis that well-defined micro-probabilities under-estimate the robust character (...)
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  23. What is Probability and Why Does It Matter.Zvonimir Šikić - 2014 - European Journal of Analytic Philosophy 10 (1):21-43.
    The idea that probability is a degree of rational belief seemed too vague for a foundation of a mathematical theory. It was certainly not obvious that degrees of rational belief had to be governed by the probability axioms as used by Laplace and other prestatistical probabilityst. The axioms seemed arbitrary in their interpretation. To eliminate the arbitrariness, the stat- isticians of the early 20th century drastically restricted the possible applications of the probability theory, by insisting that probabilities had to be (...)
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  24. Remarks on the Idealist and Empiricist Interpretation of Frequentism: Robert Leslie Ellis Versus John Venn.Lukas M. Verburgt - 2014 - BSHM Bulletin: Journal of the British Society for the History of Mathematics 29 (3):184-195.
    The goal of this paper is to correct a widespread misconception about the work of Robert Leslie Ellis and John Venn, namely that it can be considered as the ‘British empiricist’ reaction against the traditional theory of probability. It is argued, instead, that there was no unified ‘British school’ of frequentism during the nineteenth century. Where Ellis arrived at frequentism from a metaphysical idealist transformation of probability theory’s mathematical calculations, Venn did so on the basis of an empiricist critique of (...)
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  25. John Venn's Hypothetical Infinite Frequentism and Logic.Lukas M. Verburgt - 2014 - History and Philosophy of Logic 35 (3):248-271.
    The goal of this paper is to provide a detailed reading of John Venn's Logic of Chance as a work of logic or, more specifically, as a specific portion of the general system of so-called ‘material’ logic developed in his Principles of Empirical or Inductive Logic and to discuss it against the background of his Boolean-inspired views on the connection between logic and mathematics. It is by means of this situating of Venn 1866 [The Logic of Chance. An Essay on (...)
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  26. A Response to Prelec.Luc Bovens - 2013 - In Adam Oliver (ed.), Essays in Behavioural Public Policy. Cambridge: Cambridge University Press. pp. 228-33.
    At the heart of Drazen Prelec’s chapter is the distinction between outcome utility and diagnostic utility. There is a particular distinction in the literature on causal networks (Pearl 2000), namely the distinction between observing and intervening, that maps onto Prelec’s distinction between diagnostic and outcome utility. I will explore the connection between both frameworks.
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  27. Jeffrey Conditionalization, the Principal Principle, the Desire as Belief Thesis, and Adams’s Thesis.Ittay Nissan-Rozen - 2013 - British Journal for the Philosophy of Science 64 (4):axs039.
    I show that David Lewis’s principal principle is not preserved under Jeffrey conditionalization. Using this observation, I argue that Lewis’s reason for rejecting the desire as belief thesis and Adams’s thesis applies also to his own principal principle. 1 Introduction2 Adams’s Thesis, the Desire as Belief Thesis, and the Principal Principle3 Jeffrey Conditionalization4 The Principal Principles Not Preserved under Jeffrey Conditionalization5 Inadmissible Experiences.
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  28. A Frequentist Interpretation of Probability for Model-Based Inductive Inference.Aris Spanos - 2013 - Synthese 190 (9):1555-1585.
    The main objective of the paper is to propose a frequentist interpretation of probability in the context of model-based induction, anchored on the Strong Law of Large Numbers (SLLN) and justifiable on empirical grounds. It is argued that the prevailing views in philosophy of science concerning induction and the frequentist interpretation of probability are unduly influenced by enumerative induction, and the von Mises rendering, both of which are at odds with frequentist model-based induction that dominates current practice. The differences between (...)
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  29. Robert Leslie Ellis's Work on Philosophy of Science and the Foundations of Probability Theory.Lukas M. Verburgt - 2013 - Historia Mathematica 40 (4):423-454.
    The goal of this paper is to provide an extensive account of Robert Leslie Ellisʼs largely forgotten work on philosophy of science and probability theory. On the one hand, it is suggested that both his ‘idealist’ renovation of the Baconian theory of induction and a ‘realism’ vis-à-vis natural kinds were the result of a complex dialogue with the work of William Whewell. On the other hand, it is shown to what extent the combining of these two positions contributed to Ellisʼs (...)
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  30. A Philosophical Guide to Chance: Physical Probability.Toby Handfield - 2012 - Cambridge University Press.
    Contents: 1. The concept of chance; 2. The classical picture; 3. Ways the world might be; 4. Possibilities of thought; 5. Chance in phase space; 6. Possibilist theories of chance; 7. Actualist theories of chance; 8. Anti-realist theories of chance; 9. Chance in quantum physics; 10. Chance in branching worlds; 11. Time and evidence; 12. Debunking chance.
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  31. A Challenge to Ludwig von Mises’s Theory of Probability.Mark R. Crovelli - 2010 - Libertarian Papers 2:23.
    The most interesting and completely overlooked aspect of Ludwig von Mises’s theory of probability is the total absence of any explicit definition for probability in his theory. This paper examines Mises’s theory of probability in light of the fact that his theory possesses no definition for probability. It is argued, first, that Mises’s theory differs in important respects from his brother’s famous theory of probability. A defense of the subjective definition for probability is then provided, which is subsequently used to (...)
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  32. Philosophy of Probability: Contemporary Readings.Antony Eagle (ed.) - 2010 - Routledge.
    _Philosophy of Probability: Contemporary Readings_ is the first anthology to collect essential readings in this important area of philosophy. Featuring the work of leading philosophers in the field such as Carnap, Hájek, Jeffrey, Joyce, Lewis, Loewer, Popper, Ramsey, van Fraassen, von Mises, and many others, the book looks in depth at the following key topics: subjective probability and credence probability updating: conditionalization and reflection Bayesian confirmation theory classical, logical, and evidential probability frequentism physical probability: propensities and objective chances. The book (...)
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  33. The Undetectable Difference: An Experimental Look at the ‘Problem’ of P-Values.William M. Goodman - 2010 - Statistical Literacy Website/Papers: Www.Statlit.Org/Pdf/2010GoodmanASA.Pdf.
    In the face of continuing assumptions by many scientists and journal editors that p-values provide a gold standard for inference, counter warnings are published periodically. But the core problem is not with p-values, per se. A finding that “p-value is less than α” could merely signal that a critical value has been exceeded. The question is why, when estimating a parameter, we provide a range (a confidence interval), but when testing a hypothesis about a parameter (e.g. µ = x) we (...)
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  34. An Error in the Argument From Conditionality and Sufficiency to the Likelihood Principle.Deborah G. Mayo - 2010 - In Deborah G. Mayo & Aris Spanos (eds.), Error and Inference: Recent Exchanges on Experimental Reasoning, Reliability, and the Objectivity and Rationality of Science. Cambridge University Press. pp. 305.
  35. Statistical Inference Without Frequentist Justifications.Jan Sprenger - 2010 - In M. Dorato M. Suàrez (ed.), Epsa Epistemology and Methodology of Science. Springer. pp. 289--297.
    Statistical inference is often justified by long-run properties of the sampling distributions, such as the repeated sampling rationale. These are frequentist justifications of statistical inference. I argue, in line with existing philosophical literature, but against a widespread image in empirical science, that these justifications are flawed. Then I propose a novel interpretation of probability in statistics, the artefactual interpretation. I believe that this interpretation is able to bridge the gap between statistical probability calculations and rational decisions on the basis of (...)
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  36. John Maynard Keynes and Ludwig von Mises on Probability.Ludwig van den Hauwe - 2010 - Journal of Libertarian Studies 22 (1):471-507.
    The economic paradigms of Ludwig von Mises on the one hand and of John Maynard Keynes on the other have been correctly recognized as antithetical at the theoretical level, and as antagonistic with respect to their practical and public policy implications. Characteristically they have also been vindicated by opposing sides of the political spectrum. Nevertheless the respective views of these authors with respect to the meaning and interpretation of probability exhibit a closer conceptual affinity than has been acknowledged in the (...)
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  37. The Definition of Probability.Richard von Mises - 2010 - In Antony Eagle (ed.), Philosophy of Probability: Contemporary Readings. Routledge.
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  38. On the Possibility of Assigning Probabilities to Singular Cases, Or: Probability Is Subjective Too!Mark R. Crovelli - 2009 - Libertarian Papers 1:26.
    Both Ludwig von Mises and Richard von Mises claimed that numerical probability could not be legitimately applied to singular cases. This paper challenges this aspect of the von Mises brothers’ theory of probability. It is argued that their denial that numerical probability could be applied to singular cases was based solely upon Richard von Mises’ exceptionally restrictive definition of probability. This paper challenges Richard von Mises’ definition of probability by arguing that the definition of probability necessarily depends upon whether the (...)
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  39. Has David Howden Vindicated Richard von Mises’s Definition of Probability?Mark R. Crovelli - 2009 - Libertarian Papers 1:44.
    In my recent article on these pages I argued that members of the Austrian School of economics have adopted and defended a faulty definition of probability. I argued that the definition of probability necessarily depends upon the nature of the world in which we live. I claimed that if the nature of the world is such that every event and phenomenon which occurs has a cause of some sort, then probability must be defined subjectively; that is, “as a measure of (...)
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  40. Fifteen Arguments Against Hypothetical Frequentism.Alan Hájek - 2009 - Erkenntnis 70 (2):211-235.
    This is the sequel to my “Fifteen Arguments Against Finite Frequentism” ( Erkenntnis 1997), the second half of a long paper that attacks the two main forms of frequentism about probability. Hypothetical frequentism asserts: The probability of an attribute A in a reference class B is p iff the limit of the relative frequency of A ’s among the B ’s would be p if there were an infinite sequence of B ’s. I offer fifteen arguments against this analysis. I (...)
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  41. Statistics Between Inductive Logic and Empirical Science.Jan Sprenger - 2009 - Journal of Applied Logic 7 (2):239--250.
    Inductive logic generalizes the idea of logical entailment and provides standards for the evaluation of non-conclusive arguments. A main application of inductive logic is the generalization of observational data to theoretical models. In the empirical sciences, the mathematical theory of statistics addresses the same problem. This paper argues that there is no separable purely logical aspect of statistical inference in a variety of complex problems. Instead, statistical practice is often motivated by decision-theoretic considerations and resembles empirical science.
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  42. Evidence and Experimental Design in Sequential Trials.Jan Sprenger - 2009 - Philosophy of Science 76 (5):637-649.
    To what extent does the design of statistical experiments, in particular sequential trials, affect their interpretation? Should postexperimental decisions depend on the observed data alone, or should they account for the used stopping rule? Bayesians and frequentists are apparently deadlocked in their controversy over these questions. To resolve the deadlock, I suggest a three‐part strategy that combines conceptual, methodological, and decision‐theoretic arguments. This approach maintains the pre‐experimental relevance of experimental design and stopping rules but vindicates their evidential, postexperimental irrelevance. †To (...)
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  43. Reichenbach’s Posits Reposited.David Atkinson & Jeanne Peijnenburg - 2008 - Erkenntnis 69 (1):93-108.
    Reichenbach's use of 'posits' to defend his frequentistic theory of probability has been criticized on the grounds that it makes unfalsifiable predictions. The justice of this criticism has blinded many to Reichenbach's second use of a posit, one that can fruitfully be applied to current debates within epistemology. We show first that Reichenbach's alternative type of posit creates a difficulty for epistemic foundationalists, and then that its use is equivalent to a particular kind of Jeffrey conditionalization. We conclude that, under (...)
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  44. Frequentist Statistics as a Theory of Inductive Inference.Deborah G. Mayo & David Cox - 2006 - In Deborah G. Mayo & Aris Spanos (eds.), Error and Inference: Recent Exchanges on Experimental Reasoning, Reliability, and the Objectivity and Rationality of Science. Cambridge University Press.
    After some general remarks about the interrelation between philosophical and statistical thinking, the discussion centres largely on significance tests. These are defined as the calculation of p-values rather than as formal procedures for ‘acceptance‘ and ‘rejection‘. A number of types of null hypothesis are described and a principle for evidential interpretation set out governing the implications of p- values in the specific circumstances of each application, as contrasted with a long-run interpretation. A number of more complicated situ- ations are discussed (...)
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  45. Should Bayesians Bet Where Frequentists Fear to Tread?Max Albert - 2005 - Philosophy of Science 72 (4):584-593.
  46. Review: Repertoires, Frequentism, and Predictability. [REVIEW]Noeul Bonneuil - 2004 - History and Theory 43 (1):117-123.
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  47. Some Thoughts on Wesley Salmon’s Contributions to the Philosophy of Probability.Paul Humphreys - 2004 - Philosophy of Science 71 (5):942-949.
    Wesley Salmon provided three classic criteria of adequacy for satisfactory interpretations of probability. A fourth criterion is suggested here. A distinction is drawn between frequency‐driven probability models and theory‐driven probability models and it is argued that single case accounts of chance are superior to frequency accounts at least for the latter. Finally it is suggested that theories of chance should be required only to be contingently true, a position which is a natural extension of Salmon's ontic account of probabilistic causality (...)
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  48. Probability: A Philosophical Introduction.D. H. Mellor - 2004 - Routledge.
    _Probability: A Philosophical Introduction_ introduces and explains the principal concepts and applications of probability. It is intended for philosophers and others who want to understand probability as we all apply it in our working and everyday lives. The book is not a course in mathematical probability, of which it uses only the simplest results, and avoids all needless technicality. The role of probability in modern theories of knowledge, inference, induction, causation, laws of nature, action and decision-making makes an understanding of (...)
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  49. La Interpretación Frecuentista de la Probabilidad: Su Inaplicabilidad a Sucesos Singulares.José Luis Rolleri - 2003 - Signos Filosóficos 6 (11):159-171.
    The interpretation of probability, dominant in mathematics and other scientific disciplines, that defines the probability of an event S, relatively to a sequence or class of events C, as the quotient of the number of cases in which S occurs and the total number of occurrences of events in C, that..
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  50. Interpreting Probability: Controversies and Developments in the Early Twentieth Century.David Howie - 2002 - Cambridge University Press.
    The term probability can be used in two main senses. In the frequency interpretation it is a limiting ratio in a sequence of repeatable events. In the Bayesian view, probability is a mental construct representing uncertainty. This 2002 book is about these two types of probability and investigates how, despite being adopted by scientists and statisticians in the eighteenth and nineteenth centuries, Bayesianism was discredited as a theory of scientific inference during the 1920s and 1930s. Through the examination of a (...)
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