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  1. 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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  2. Probability and Causality. J. H. Fetzer.Frank Arntzenius - 1990 - Philosophy of Science 57 (2):338-340.
    This is a book review of J. H. Fetzer (ed) Probability and Causality.
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  3. A World of Probability: Experience and Prediction By Hans Reichenbach. [REVIEW]Eleanor Bisbee - 1938 - Philosophy of Science 5 (3):360-366.
  4. Disentangling Mechanisms From Causes: And the Effects on Science.John Protzko - 2018 - Foundations of Science 23 (1):37-50.
    Despite the miraculous progress of science—it’s practitioners continue to run into mistakes, either discrediting research unduly or making leaps of causal inference where none are warranted. In this we isolate two of the reasons for such behavior involving the misplaced understanding of the role of mechanisms and mechanistic knowledge in the establishment of cause-effect relationships. We differentiate causal knowledge into causes, effects, mechanisms, cause-effect relationships, and causal stories. Failing to understand the role of mechanisms in this picture, including their absence (...)
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  5. The Qualitative Paradox of Non-Conglomerability.Nicholas DiBella - 2018 - Synthese 195 (3):1181-1210.
    A probability function is non-conglomerable just in case there is some proposition E and partition \ of the space of possible outcomes such that the probability of E conditional on any member of \ is bounded by two values yet the unconditional probability of E is not bounded by those values. The paradox of non-conglomerability is the counterintuitive—and controversial—claim that a rational agent’s subjective probability function can be non-conglomerable. In this paper, I present a qualitative analogue of the paradox. I (...)
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  6. II.—On a Defect in the Customary Logical Formulation of Inductive Reasoning.Bernard Bosanquet - 1911 - Proceedings of the Aristotelian Society 11 (1):29-40.
  7. Discussion: The Non-Reducibility of Koopman's Theorems of Probability in Carnap's System for Mc.Robert M. Jones - 1965 - Philosophy of Science 32 (3/4):368.
  8. Probability Logic.Douglas N. Hoover - 1978 - Annals of Mathematical Logic 14 (3):287.
  9. Theory Versions Instead of Articulations of a Paradigm.Ruey-lin Chen - 2000 - Studies in History and Philosophy of Science Part A 31 (3):449-471.
  10. Folk Judgments of Causation.Joshua Knobe - 2009 - Studies in History and Philosophy of Science Part A 40 (2):238-242.
    Experimental studies suggest that people’s ordinary causal judgments are affected not only by statistical considerations but also by moral considerations. One way to explain these results would be to construct a model according to which people are trying to make a purely statistical judgment but moral considerations somehow distort their intuitions. The present paper offers an alternative perspective. Specifically, the author proposes a model according to which the very same underlying mechanism accounts for the influence of both statistical and moral (...)
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  11. Metaphysics and Probability, ‘Meaning’ and ‘Justification’.John King-Farlow - 1971 - Philosophical Studies (Dublin) 20:203-209.
    TZIPORAH KASACHKOFF’S most welcome and valuable attack on D Z Phillips’ ‘internalist’ defence of his strange forms of fideism closes with these words.
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  12. 2. Logic and Probability.Richard Johns - 2002 - In A Theory of Physical Probability. University of Toronto Press. pp. 9-52.
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  13. An Internal Limit of the Structural Analysis of Causation.Alessandro Giordani - 2016 - Axiomathes 26 (4):429-450.
    Structural models of systems of causal connections have become a common tool in the analysis of the concept of causation. In the present paper I offer a general argument to show that one of the most powerful definitions of the concept of actual cause, provided within the structural models framework, is not sufficient to grant a full account of our intuitive judgements about actual causation, so that we are still waiting for a comprehensive definition. This is done not simply by (...)
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  14. Nomic Probability and the Foundations of Induction. [REVIEW]Henry E. Kyburg & John L. Pollock - 1993 - Philosophical Review 102 (1):115.
  15. Scientific Inference.Stephen F. Barker - 1958 - Philosophical Review 67 (3):404.
  16. A Treatise on Probability.Clarence Irving Lewis - 1922 - Philosophical Review 31 (2):180.
  17. Bayes Factors All the Way: Toward a New View of Coherence and Truth.Lydia McGrew - 2016 - Theoria 82 (4):329-350.
    A focus on the conjunction of the contents of witness reports and on the coherence of their contents has had negative effects on the epistemic clarity of the Bayesian coherence literature. Whether or not increased coherence of witness reports is correlated with higher confirmation for some H depends upon the hypothesis in question and upon factors concerning the confirmation and independence of the reports, not directly on the positive relevance of the contents to each other. I suggest that Bayesians should (...)
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  18. Understanding Probability: Darrell Rowbottom: Probability. Cambridge and Malden: Polity Press, 2015, 180pp, $64.95 HB, $22.95 PB.Justin Dallmann - 2016 - Metascience 25 (2):331-333.
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  19. Seismic Chronostratigraphy at Reservoir Scale: Statistical Modeling.Yawen He, Hongliu Zeng, Charles Kerans & Bob A. Hardage - 2015 - Interpretation: SEG 3 (2):SN69-SN87.
    We have developed a statistical modeling method to quantify the chronostratigraphic meaning of primary seismic reflections, with an emphasis on applications at reservoir scale. The time-correlation error is defined as the difference between seismic events and relative geologic time. A series of statistically simulated impedance models with flat chronostratigraphic surfaces was generated from a subsurface data set to describe gradual lithofacies changes in contemporaneous strata and to account for vertical cyclicity from seed wireline logs. We converted these models to realistic (...)
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  20. Probability and Paradox.F. Granger - 1929 - Aristotelian Society Supplementary Volume 9 (1):1-18.
  21. A Probabilistic Explanation for the Size-Effect in Crystal Plasticity.P. M. Derlet & R. Maaß - 2015 - Philosophical Magazine 95 (16-18):1829-1844.
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  22. XIV—Probability as the Logic of Science.Mary Hesse - 1972 - Proceedings of the Aristotelian Society 72 (1):257-272.
  23. Curve Fitting by Means of the Orthogonal Polynomials in Binomial Statistical Distributions.H. T. Gonin - 1943 - Transactions of the Royal Society of South Africa 30 (3):207-215.
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  24. Epistemic Inconsistency and Categorical Coherence: A Study of Probabilistic Measures of Coherence.Michael Hughes - 2017 - Synthese 194 (8):3153-3185.
    Is logical consistency required for a set of beliefs or propositions to be categorically coherent? An affirmative answer is often assumed by mainstream epistemologists, and yet it is unclear why. Cases like the lottery and the preface call into question the assumption that beliefs must be consistent in order to be epistemically rational. And thus it is natural to wonder why all inconsistent sets of propositions are incoherent. On the other hand, Easwaran and Fitelson have shown that particular kinds of (...)
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  25. Assessing Uncertainty.Amos Tversky - 1974 - Journal of the Royal Statistical Society. Series B 36 (2):148-159.
    Intuitive judgments of probability are based on a limited number of heuristics that are usually effective but sometimes lead to severe and systematic errors. Research shows, for example, that people judge the probability of a hypothesis by the degree to which it represents the evidence, with little or no regard for its prior probability. Other heuristics lead to an overestimation of the probabilities of highly available or salient events, and to overconfidence in the assessment of subjective probability distributions. These biases (...)
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  26. Chapter 8. Relative Probability Functions Construed as Representing Degrees of Logical Consequence.Peter Roeper & Hughes Leblanc - 1999 - In Peter Roeper & Hughes Leblanc (eds.), Probability Theory and Probability Semantics. University of Toronto Press. pp. 142-166.
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  27. Probability and Evidence.Paul Horwich - 1982 - Cambridge University Press.
    In this influential study of central issues in the philosophy of science, Paul Horwich elaborates on an important conception of probability, diagnosing the failure of previous attempts to resolve these issues as stemming from a too-rigid conception of belief. Adopting a Bayesian strategy, he argues for a probabilistic approach, yielding a more complete understanding of the characteristics of scientific reasoning and methodology. Presented in a fresh twenty-first-century series livery, and including a specially commissioned preface written by Colin Howson, illuminating its (...)
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  28. Does Causal Knowledge Help Us Be Faster and More Frugal in Our Decisions?Rocio Garcia-Retamero, Annika Wallin & Anja Dieckmann - unknown
    One challenge that has to be addressed by the fast and frugal heuristics program is how people manage to select, from the abundance of cues that exist in the environment, those to rely on when making decisions. We hypothesize that causal knowledge helps people target particular cues and estimate their validities. This hypothesis was tested in three experiments. Results show that when causal information about some cues was available, participants preferred to search for these cues first and to base their (...)
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  29. Causal Reasoning with Ancestral Graphical Models.Jiji Zhang - 2008 - Journal of Machine Learning Research 9:1437-1474.
    Causal reasoning is primarily concerned with what would happen to a system under external interventions. In particular, we are often interested in predicting the probability distribution of some random variables that would result if some other variables were forced to take certain values. One prominent approach to tackling this problem is based on causal Bayesian networks, using directed acyclic graphs as causal diagrams to relate post-intervention probabilities to pre-intervention probabilities that are estimable from observational data. However, such causal diagrams are (...)
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  30. A Note on Cancellation Axioms for Comparative Probability.Matthew Harrison-Trainor, Wesley H. Holliday & Thomas F. Icard - 2016 - Theory and Decision 80 (1):159-166.
    We prove that the generalized cancellation axiom for incomplete comparative probability relations introduced by Rios Insua and Alon and Lehrer is stronger than the standard cancellation axiom for complete comparative probability relations introduced by Scott, 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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  31. Shogenji's Probabilistic Measure of Coherence is Incoherent.K. Akiba - 2000 - Analysis 60 (4):356-359.
  32. Carnap's Inductive Probabilities as a Contribution to Decision Theory.Joachim Hornung - 1980 - Metamedicine 1 (3):325-367.
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  33. Counterfactuals and Epistemic Probability.R. Otte - 2006 - Synthese 152 (1):81-93.
    Philosophers have often attempted to use counterfactual conditionals to analyze probability. This article focuses on counterfactual analyzes of epistemic probability by Alvin Plantinga and Peter van Inwagen. I argue that a certain type of counterfactual situation creates problems for these analyses. I then argue that Plantinga's intuition about the role of warrant in epistemic probability is mistaken. Both van Inwagen's and Plantinga's intuitions about epistemic probability are flawed.
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  34. From Knowledge-Based Programs to Graded Belief-Based Programs, Part I: On-Line Reasoning.Noël Laverny & Jérôme Lang - 2005 - Synthese 147 (2):277-321.
    Knowledge-based programs (KBPs) are a powerful notion for expressing action policies in which branching conditions refer to implicit knowledge and call for a deliberation task at execution time. However, branching conditions in KBPs cannot refer to possibly erroneous beliefs or to graded belief, such as “if my belief that φ holds is high then do some action α else perform some sensing action β”.
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  35. Against Probabilistic Measures of Coherence.Mark Siebel - 2005 - Erkenntnis 63 (3):335-360.
    It is shown that the probabilistic theories of coherence proposed up to now produce a number of counter-intuitive results. The last section provides some reasons for believing that no probabilistic measure will ever be able to adequately capture coherence. First, there can be no function whose arguments are nothing but tuples of probabilities, and which assigns different values to pairs of propositions {A, B} and {A, C} if A implies both B and C, or their negations, and if P(B)=P(C). But (...)
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  36. Probability and the Science of Logic.Izumi Takeuchi - 2012 - Kagaku Tetsugaku 45 (2):1-13.
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  37. Probabilistic Coherence Measures: A Psychological Study of Coherence Assessment.Jakob Koscholke & Marc Jekel - 2017 - Synthese 194 (4).
    Over the years several non-equivalent probabilistic measures of coherence have been discussed in the philosophical literature. In this paper we examine these measures with respect to their empirical adequacy. Using test cases from the coherence literature as vignettes for psychological experiments we investigate whether the measures can predict the subjective coherence assessments of the participants. It turns out that the participants’ coherence assessments are best described by Roche’s coherence measure based on Douven and Meijs’ average mutual support approach and the (...)
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  38. Reliability Conducive Measures of Coherence.Erik J. Olsson & Stefan Schubert - 2007 - Synthese 157 (3):297-308.
    A measure of coherence is said to be truth conducive if and only if a higher degree of coherence results in a higher likelihood of truth. Recent impossibility results strongly indicate that there are no probabilistic coherence measures that are truth conducive. Indeed, this holds even if truth conduciveness is understood in a weak ceteris paribus sense. This raises the problem of how coherence could nonetheless be an epistemically important property. Our proposal is that coherence may be linked in a (...)
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  39. Error Statistical Modeling and Inference: Where Methodology Meets Ontology.Aris Spanos & Deborah G. Mayo - 2015 - Synthese 192 (11):3533-3555.
    In empirical modeling, an important desiderata for deeming theoretical entities and processes as real is that they can be reproducible in a statistical sense. Current day crises regarding replicability in science intertwines with the question of how statistical methods link data to statistical and substantive theories and models. Different answers to this question have important methodological consequences for inference, which are intertwined with a contrast between the ontological commitments of the two types of models. The key to untangling them is (...)
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  40. The Ontological Status of Shocks and Trends in Macroeconomics.Kevin D. Hoover - 2015 - Synthese 192 (11):3509-3532.
    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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  41. The Emergence of Probability: A Philosophical Study of Early Ideas About Probability, Induction and Statistical Inference.Ian Hacking - 1975 - Cambridge University Press.
    Historical records show that there was no real concept of probability in Europe before the mid-seventeenth century, although the use of dice and other randomizing objects was commonplace. Ian Hacking presents a philosophical critique of early ideas about probability, induction, and statistical inference and the growth of this new family of ideas in the fifteenth, sixteenth, and seventeenth centuries. Hacking invokes a wide intellectual framework involving the growth of science, economics, and the theology of the period. He argues that the (...)
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  42. Scientific Inference.Harold Jeffreys - 2011 - Cambridge University Press.
    A scientific theory is originally based on a particular set of observations. How can it be extended to apply outside this original range of cases? This question, which is fundamental to natural philosophy, is considered in detail in this book, which was originally published in 1931, and first published as this third edition in 1973. Sir Harold begins with the principle that 'it is possible to learn from experience and to make inferences from beyond the data directly known to sensation'. (...)
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  43. Probability and Content Measure.Rudolf Carnap - 1966 - In Paul K. Feyerabend & Grover Maxwell (eds.), Mind, Matter and Method: Essays in Philosophy and Science in Honor of Herbert Feigl. University of Minnesota Press: Minneapolis. pp. 248--260.
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  44. Logic and Probability.Lorenz Demey, Barteld Kooi & Joshua Sack - 2014 - In Edward N. Zalta (ed.), The Stanford Encyclopedia of Philosophy.
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  45. What Is Going on Inside the Arrows? Discovering the Hidden Springs in Causal Models.Alexander Murray-Watters & Clark Glymour - 2015 - Philosophy of Science 82 (4):556-586.
    Using Gebharter’s representation, we consider aspects of the problem of discovering the structure of unmeasured submechanisms when the variables in those submechanisms have not been measured. Exploiting an early insight of Sober’s, we provide a correct algorithm for identifying latent, endogenous structure—submechanisms—for a restricted class of structures. The algorithm can be merged with other methods for discovering causal relations among unmeasured variables, and feedback relations between measured variables and unobserved causes can sometimes be learned.
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  46. On the Incompatibility of Dynamical Biological Mechanisms and Causal Graph Theory.Marcel Weber - unknown
    I examine the adequacy of the causal graph-structural equations approach to causation for modeling biological mechanisms. I focus in particular on mechanisms with complex dynamics such as the PER biological clock mechanism in Drosophila. I show that a quantitative model of this mechanism that uses coupled differential equations – the well-known Goldbeter model – cannot be adequately represented in the standard causal graph framework, even though this framework does permit causal cycles. The reason is that the model contains dynamical information (...)
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  47. Likelihood and Consilience: On Forster’s Counterexamples to the Likelihood Theory of Evidence.Jiji Zhang & Kun Zhang - 2015 - Philosophy of Science 82 (5):930-940.
    Forster presented some interesting examples having to do with distinguishing the direction of causal influence between two variables, which he argued are counterexamples to the likelihood theory of evidence. In this paper, we refute Forster's arguments by carefully examining one of the alleged counterexamples. We argue that the example is not convincing as it relies on dubious intuitions that likelihoodists have forcefully criticized. More importantly, we show that contrary to Forster's contention, the consilience-based methodology he favored is accountable within the (...)
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  48. Probability.Darrell Patrick Rowbottom - 2015 - Polity.
    When a doctor tells you there’s a one percent chance that an operation will result in your death, or a scientist claims that his theory is probably true, what exactly does that mean? Understanding probability is clearly very important, if we are to make good theoretical and practical choices. In this engaging and highly accessible introduction to the philosophy of probability, Darrell Rowbottom takes the reader on a journey through all the major interpretations of probability, with reference to real–world situations. (...)
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  49. Reconstructing The Past: Parsimony, Evolution, and Inference.Elliott Sober - 1988 - MIT Press.
  50. Probability Logic.Kazem Sadegh-Zadeh - 2nd ed. 2015 - In Handbook of Analytic Philosophy of Medicine. Springer Verlag.
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