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  1. A. R. A. (1957). Probability in Logic. Review of Metaphysics 11 (2):348-348.
  2. R. A. A. (1957). Probability in Logic. [REVIEW] Review of Metaphysics 11 (2):348-348.
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  3. Ken Akiba (2000). Shogenji's Probabilistic Measure of Coherence is Incoherent. Analysis 60 (4):356–359.
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  4. Staffan Angere (2008). Coherence as a Heuristic. Mind 117 (465):1-26.
    The impossibility results of Bovens and Hartmann (2003) and Olsson (2005) call into question the strength of the connection between coherence and truth. As part of the inquiry into this alleged link, I define a notion of degree of truth-conduciveness, relevant for measuring the usefulness of coherence measures as rules-of-thumb for assigning probabilities in situations of partial knowledge. I use the concept to compare the viability of some of the measures of coherence that have been suggested so far under different (...)
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  5. Staffan Angere (2007). The Defeasible Nature of Coherentist Justification. Synthese 157 (3):321 - 335.
    The impossibility results of Bovens and Hartmann (2003, Bayesian epistemology. Oxford: Clarendon Press) and Olsson (2005, Against coherence: Truth, probability and justification. Oxford: Oxford University Press.) show that the link between coherence and probability is not as strong as some have supposed. This paper is an attempt to bring out a way in which coherence reasoning nevertheless can be justified, based on the idea that, even if it does not provide an infallible guide to probability, it can give us an (...)
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  6. N. D. B. (1961). Markov Learning Models for Multiperson Interactions. Review of Metaphysics 15 (1):196-196.
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  7. Patricia Baillie (1971). Confirmation and Probability: A Reply to Settle. British Journal for the Philosophy of Science 22 (3):285-286.
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  8. Jonathan Baron (1987). Second-Order Probabilities and Belief Functions. Theory and Decision 23 (1):25-36.
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  9. M. S. Bartlett (1949). Probability in Logic, Mathematics and Science. Dialectica 3 (1‐2):104-113.
    Historically the emergence of a precise technical meaning for probability, as distinct from its vague popular useage, has taken time; and confusion still arises from the concept of probability having different meanings in different flelds of discourse. Its technical meaning and appropriate rules are surveyed in the flelds of logic , mathematics , and science , and the relation between these three aspects of probability theory discussed. ‐. M. S. B.
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  10. Marianne Belis (2007). The Causal Roots of Probability. In Federica Russo & Jon Williamson (eds.), Causality and Probability in the Sciences. 5--295.
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  11. Gerry Curtis Bridgeman (2001). Practical Reasoning Through Coherent Goal Specification. Dissertation, Vanderbilt University
    In this work, I try to further specify what practical coherence should amount to. Any account of practical reasoning ought to be able to say something about how we ought to go about specifying our goals. One possibility is coherence theory. But coherence theory as it is normally conceived cannot be sufficient for rationality. Practical reasoning, which takes place over time, poses special difficulties for the coherence theorist. There is a danger that unless coherence theory has some element of stability (...)
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  12. Peter Brugger & Kirsten I. Taylor (2003). ESP: Extrasensory Perception or Effect of Subjective Probability? Journal of Consciousness Studies 10 (6-7):6-7.
    This paper consists of two parts. In the first, we discuss the neuropsychological correlates of belief in a 'paranormal' or magical causation of coincidences. In particular, we review experimental evidence demonstrating that believers in ESP and kindred forms of paranormal phenomena differ from disbelievers with respect to indices of sequential response production and semantic-associative processing. Not only do believers judge artificial coincidences as more 'meaningful' than disbelievers, they also more strongly suppress coincidental productions (i.e. repetitions) in their generation of random (...)
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  13. E. Brunswik (1939). Probability as a Determiner of Rat Behavior. Journal of Experimental Psychology 25 (2):175.
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  14. B. C. (1982). Philosophical Problems of Statistical Inference. Review of Metaphysics 35 (4):907-909.
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  15. Andrea Capotorti, Giulianella Coletti & Barbara Vantaggi (2008). Preferences Representable by a Lower Expectation: Some Characterizations. [REVIEW] Theory and Decision 64 (2-3):119-146.
    We propose two different characterizations for preference relations representable by lower (upper) expectations with the aim of removing either fair price or completeness requirements. Moreover, we give an explicit characterization for comparative degrees of belief on a finite algebra of events representable by lower probabilities.
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  16. Bruno De Finetti (2008). Philosophical Lectures on Probability. Collected, Edited and Annotated by Alberto Mura. Springer.
    The book contains the transcription of a course on the foundations of probability given by the Italian mathematician Bruno de Finetti in 1979 at the a oeNational Institute of Advanced Mathematicsa in Rome.
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  17. Lorenz Demey, Barteld Kooi & Joshua Sack (forthcoming). Logic and Probability. Stanford Encyclopedia of Philosophy.
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  18. Mary Deutsch-McLeish (1991). A Study of Probabilities and Belief Functions Under Conflicting Evidence: Comparisons and New Methods. In B. Bouchon-Meunier, R. R. Yager & L. A. Zadeh (eds.), Uncertainty in Knowledge Bases. Springer. 41--49.
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  19. Igor Douven (2005). Basic Beliefs, Coherence, and Bootstrap Confirmation. In Rene van Woudenberg, Sabine Roeser & Ron Rood (eds.), Basic Belief and Basic Knowledge. Ontos-Verlag. 4--57.
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  20. Fred Dretske (1989). The Likelihood of Knowledge. Review of Metaphysics 42 (3):632-633.
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  21. Kenny Easwaran (2014). Probability and Logic. Philosophy Compass 9 (12):876-883.
    Probability and logic are two branches of mathematics that have important philosophical applications. This article discusses several areas of intersection between them. Several involve the role for probability in giving semantics for logic or the role of logic in governing assignments of probability. Some involve probability over non-classical logic or self-referential sentences.
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  22. Leif Eriksen (1989). Confirmation, Paradox, and Logic. Philosophy of Science 56 (4):681-687.
    Paul Horwich has formulated a paradox which he believes to be even more virulent than the related Hempel paradox. I show that Horwich's paradox, as orginally formulated, has a purely logical solution, hence that it has no bearing on the theory of confirmation. On the other hand, it illuminates some undesirable traits of classical predicate logic. A revised formulation of the paradox is then dealt with in a way that implies a modest revision of Nicod's criterion.
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  23. Branden Fitelson, Dempster-Shafer Functions as Metalinguistic Probability Functions.
    Let Ln be a sentential language with n atomic sentences {A1, . . . , An}. Let Sn = {s1, . . . , s2n} be the set of 2n state descriptions of Ln, in the following, canonical lexicographical truth-table order: State Description A1 A2 · · · An−1 An T T T T T s1 = A1 & A2 & · · · &An−1 & An T T T T F s1 = A1 & A2 & · · · (...)
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  24. Branden Fitelson, Probabilistic Coherence From a Logical Point of View.
    – Foundation: Probabilistic Confirmation (c) from a Logical POV ∗ cph, eq as a “relevant” quantitative generalization of pe  hq ∗ cph, eq, so understood, is not Prpe  hq or Prph | eq, etc. ∗ cph, eq is something akin (ordinally) to the likelihood ratio..
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  25. Branden Fitelson (2003). A Probabilistic Theory of Coherence. Analysis 63 (3):194–199.
    Let E be a set of n propositions E1, ..., En. We seek a probabilistic measure C(E) of the ‘degree of coherence’ of E. Intuitively, we want C to be a quantitative, probabilistic generalization of the (deductive) logical coherence of E. So, in particular, we require C to satisfy the following..
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  26. Malcolm Forster, Chapter 3: Simplicity and Unification in Model Selection.
    This chapter examines four solutions to the problem of many models, and finds some fault or limitation with all of them except the last. The first is the naïve empiricist view that best model is the one that best fits the data. The second is based on Popper’s falsificationism. The third approach is to compare models on the basis of some kind of trade off between fit and simplicity. The fourth is the most powerful: Cross validation testing.
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  27. Malcolm R. Forster (1995). The Golfer's Dilemma: A Reply to Kukla on Curve-Fitting. British Journal for the Philosophy of Science 46 (3):348-360.
    Curve-fitting typically works by trading off goodness-of-fit with simplicity, where simplicity is measured by the number of adjustable parameters. However, such methods cannot be applied in an unrestricted way. I discuss one such correction, and explain why the exception arises. The same kind of probabilistic explanation offers a surprising resolution to a common-sense dilemma.
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  28. Patrizio Frederic, Mario Di Bacco & Frank Lad (2012). Combining Expert Probabilities Using the Product of Odds. Theory and Decision 73 (4):605-619.
    We resolve a useful formulation of the question how a statistician can coherently incorporate the information in a consulted expert’s probability assessment for an event into a personal posterior probability assertion. Using a framework that recognises the total information available as composed of units available only to each of them along with units available to both, we show: that a sufficient statistic for all the information available to both the expert and the statistician is the product of their odds ratios (...)
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  29. Maria Carla Galavotti, Roberto Scazzieri & Patrick Suppes (eds.) (2008). Reasoning, Rationality and Probability. Center for the Study of Language and Inf.
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  30. Richard T. De George (1990). Ethics and Coherence. Proceedings and Addresses of the American Philosophical Association 64 (3):39 - 52.
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  31. Gianluca Giorgolo, Shalom Lappin & Alexander Clark, Towards a Statistical Model of Grammaticality.
    The question of whether it is possible to characterise grammatical knowledge in probabilistic terms is central to determining the relationship of linguistic representation to other cognitive domains. We present a statistical model of grammaticality which maps the probabilities of a statistical model for sentences in parts of the British National Corpus (BNC) into grammaticality scores, using various functions of the parameters of the model. We test this approach with a classifier on test sets containing different levels of syntactic infelicity. With (...)
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  32. David H. Glass (2012). Inference to the Best Explanation: Does It Track Truth? Synthese 185 (3):411-427.
    In the form of inference known as inference to the best explanation there are various ways to characterise what is meant by the best explanation. This paper considers a number of such characterisations including several based on confirmation measures and several based on coherence measures. The goal is to find a measure which adequately captures what is meant by 'best' and which also yields the truth with a high degree of probability. Computer simulations are used to show that the overlap (...)
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  33. Clark Glymour (2009). Causation and Statistical Inference. In Helen Beebee, Christopher Hitchcock & Peter Menzies (eds.), The Oxford Handbook of Causation. Oup Oxford.
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  34. Matthew Harrison-Trainor, Wesley H. Holliday & Thomas F. Icard (forthcoming). A Note on Cancellation Axioms for Comparative Probability. Theory and Decision.
    We prove that the generalized cancellation axiom for incomplete comparative probability relations introduced by Rios Insua (1992) and Alon and Lehrer (2014) is stronger than the standard cancellation axiom for complete comparative probability relations introduced by Scott (1964), relative to their other axioms for comparative probability in both the finite and infinite cases. This result has been suggested but not proved in the previous literature.
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  35. Douglas N. Hoover (1978). Probability Logic. Annals of Mathematical Logic 14 (3):287-313.
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  36. Kevin D. Hoover (forthcoming). The Ontological Status of Shocks and Trends in Macroeconomics. Synthese:1-24.
    Modern empirical macroeconomic models, known as structural autoregressions (SVARs) are dynamic models that typically claim to represent a causal order among contemporaneously valued variables and to merely represent non-structural (reduced-form) co-occurence between lagged variables and contemporaneous variables. The strategy is held to meet the minimal requirements for identifying the residual errors in particular equations in the model with independent, though otherwise not directly observable, exogenous causes (“shocks”) that ultimately account for change in the model. In nonstationary models, such shocks accumulate (...)
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  37. Kevin D. Hoover (2012). Causal Structure and Hierarchies of Models. Studies in History and Philosophy of Science Part C 43 (4):778-786.
    Economics prefers complete explanations: general over partial equilibrium, microfoundational over aggregate. Similarly, probabilistic accounts of causation frequently prefer greater detail to less as in typical resolutions of Simpson’s paradox. Strategies of causal refinement equally aim to distinguish direct from indirect causes. Yet, there are countervailing practices in economics. Representative-agent models aim to capture economic motivation but not to reduce the level of aggregation. Small structural vector-autoregression and dynamic stochastic general-equilibrium models are practically preferred to larger ones. The distinction between exogenous (...)
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  38. Elizabeth Irvine (2013). Measures of Consciousness. Philosophy Compass 8 (3):285-297.
    Consciousness is now a hot topic in both philosophy and the cognitive sciences, yet there is much controversy over how to measure it. First, it is not clear whether biased subjective reports should be taken as adequate for measuring consciousness, or if more objective measures are required. Ways to benefit from the advantages of both these measures in the form of ‘Type 2’ metacognitive measures are under development, but face criticism. Research into neurophysiological measures of consciousness is potentially very valuable, (...)
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  39. Richard C. Jeffrey (1969). Statistical Explanation Vs. Statistical Inference. In Nicholas Rescher (ed.), Essays in Honor of Carl G. Hempel. Reidel. 104--113.
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  40. L. John (2002). Causal Probability. Synthese 132 (1-2):1-2.
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  41. Mark Kaplan (2008). Confidence and Probability. In Duncan Pritchard & Ram Neta (eds.), Arguing About Knowledge. Routledge. 127.
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  42. Michael Harry Kelley (1969). Methodological Problems of Logical Probability. Dissertation, The University of Wisconsin - Madison
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  43. Robert Kennes & Philippe Smets (1991). Fast Algorithms for Dempster-Shafer Theory. In B. Bouchon-Meunier, R. R. Yager & L. A. Zadeh (eds.), Uncertainty in Knowledge Bases. Springer. 14--23.
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  44. I. A. Kieseppä (1997). Akaike Information Criterion, Curve-Fitting, and the Philosophical Problem of Simplicity. British Journal for the Philosophy of Science 48 (1):21-48.
    The philosophical significance of the procedure of applying Akaike Information Criterion (AIC) to curve-fitting problems is evaluated. The theoretical justification for using AIC (the so-called Akaike's theorem) is presented in a rigorous way, and its range of validity is assessed by presenting both instances in which it is valid and counter-examples in which it is invalid. The philosophical relevance of the justification that this result gives for making one particular choice between simple and complicated hypotheses is emphasized. In addition, recent (...)
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  45. T. A. F. Kuipers (1971). Inductive Probability and the Paradox of Ideal Confirmation. Philosophica 17 (1):197-205.
  46. Łukasz Kukier, Marek Szydłowski & Paweł Tambor (2009). Kryterium Akaike: prostota w języku statystyki. Roczniki Filozoficzne 57 (1):91-126.
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  47. André Kukla (1995). Forster and Sober on the Curve-Fitting Problem. British Journal for the Philosophy of Science 46 (2):248-252.
    Forster and Sober present a solution to the curve-fitting problem based on Akaike's Theorem. Their analysis shows that the curve with the best epistemic credentials need not always be the curve that most closely fits the data. However, their solution does not, without further argument, avoid the two difficulties that are traditionally associated with the curve-fitting problem: that there are infinitely many equally good candidate-curves relative to any given set of data, and that these best candidates include curves with indefinitely (...)
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  48. Henry E. Kyburg (1991). Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference by Judea Pearl. Journal of Philosophy 88 (8):434-437.
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  49. Keith Lehrer (1980). Coherence and the Racehorse Paradox. Midwest Studies in Philosophy 5 (1):183-192.
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  50. Bert Leuridan (2007). Galton's Blinding Glasses. Modern Statistics Hiding Causal Structure in Early Theories of Inheritance. In Federica Russo & Jon Williamson (eds.), Causality and Probability in the Sciences. 243--262.
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