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  1. A Gentle Approach to Imprecise Probabilities.Gregory Wheeler - forthcoming - In Thomas Augustin, Fabio Cozman & Gregory Wheeler (eds.), Reflections on the Foundations of Probability and Statistics: Essays in Honor of Teddy Seidenfeld. Springer.
    The field of of imprecise probability has matured, in no small part because of Teddy Seidenfeld’s decades of original scholarship and essential contributions to building and sustaining the ISIPTA community. Although the basic idea behind imprecise probability is (at least) 150 years old, a mature mathematical theory has only taken full form in the last 30 years. Interest in imprecise probability during this period has also grown, but many of the ideas that the mature theory serves can be difficult to (...)
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  2. Luzin’s (N) and Randomness Reflection.Arno Pauly, Linda Westrick & Liang Yu - 2020 - Journal of Symbolic Logic:1-27.
    We show that a computable function $f:\mathbb R\rightarrow \mathbb R$ has Luzin’s property if and only if it reflects $\Pi ^1_1$ -randomness, if and only if it reflects $\Delta ^1_1$ -randomness, and if and only if it reflects ${\mathcal {O}}$ -Kurtz randomness, but reflecting Martin–Löf randomness or weak-2-randomness does not suffice. Here a function f is said to reflect a randomness notion R if whenever $f$ is R-random, then x is R-random as well. If additionally f is known to have (...)
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  3. The Complex Nexus of Evolutionary Fitness.Mauricio Suárez - 2022 - European Journal for Philosophy of Science 12 (1):1-26.
    The propensity nature of evolutionary fitness has long been appreciated and is nowadays amply discussed. The discussion has, however, on occasion followed long standing conflations in the philosophy of probability literature between propensities, probabilities, and frequencies. In this paper, I apply a more recent conception of propensities in modelling practice to some of the key issues, regarding the mathematical representation of fitness and how it may be regarded as explanatory. The ensuing complex nexus of fitness emphasises the distinction between biological (...)
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  4. Probabilistic Inferences From Conjoined to Iterated Conditionals.Giuseppe Sanfilippo - 2018 - International Journal of Approximate Reasoning 93:103-118.
    There is wide support in logic, philosophy, and psychology for the hypothesis that the probability of the indicative conditional of natural language, $P(\textit{if } A \textit{ then } B)$, is the conditional probability of $B$ given $A$, $P(B|A)$. We identify a conditional which is such that $P(\textit{if } A \textit{ then } B)= P(B|A)$ with de Finetti's conditional event, $B|A$. An objection to making this identification in the past was that it appeared unclear how to form compounds and iterations of (...)
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  5. Философски поглед към въвеждането на отрицателна и комплексна вероятност в квантовата информация.Vasil Penchev - 2012 - Philosophical Alternatives 21 (1):63-78.
    Математическата величина на вероятността се определя стандартно като положително реално число в затворения интервал от нула до единица, еднозначно опредимо в съотвествие с няколко аксиоми, напр. тези на Колмпгоров. Нейната философска интерпретация е на мярка за част от цяло. В теорията на квантовата информация, изследваща явленията на сдвояване [entanglement] в квантовата механика, се въвеждат отрицателни и комплексни вероятности. Статията обсъжда проблема какво би следвало да бъде тяхното релевантно философско тълкувание.
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  6. Infinitesimal Probabilities.Vieri Benci, Leon Horsten & Sylvia Wenmackers - 2016 - British Journal for the Philosophy of Science 69 (2):509-552.
    Non-Archimedean probability functions allow us to combine regularity with perfect additivity. We discuss the philosophical motivation for a particular choice of axioms for a non-Archimedean probability theory and answer some philosophical objections that have been raised against infinitesimal probabilities in general. _1_ Introduction _2_ The Limits of Classical Probability Theory _2.1_ Classical probability functions _2.2_ Limitations _2.3_ Infinitesimals to the rescue? _3_ NAP Theory _3.1_ First four axioms of NAP _3.2_ Continuity and conditional probability _3.3_ The final axiom of NAP (...)
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  7. Relating Bell’s Local Causality to the Causal Markov Condition.Gábor Hofer-Szabó - 2015 - Foundations of Physics 45 (9):1110-1136.
    The aim of the paper is to relate Bell’s notion of local causality to the Causal Markov Condition. To this end, first a framework, called local physical theory, will be introduced integrating spatiotemporal and probabilistic entities and the notions of local causality and Markovity will be defined. Then, illustrated in a simple stochastic model, it will be shown how a discrete local physical theory transforms into a Bayesian network and how the Causal Markov Condition arises as a special case of (...)
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  8. David Papineau. Philosophical Devices: Proofs, Probabilities, Possibilities, and Sets. Oxford: Oxford University Press, 2012. ISBN 978-0-19965173-3. Pp. Xix + 224. [REVIEW]A. C. Paseau - 2013 - Philosophia Mathematica (1):nkt006.
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  9. Incompatibility of the Schrödinger Equation with Langevin and Fokker-Planck Equations.Daniel T. Gillespie - 1995 - Foundations of Physics 25 (7):1041-1053.
    Quantum mechanics posits that the wave function of a one-particle system evolves with time according to the Schrödinger equation, and furthermore has a square modulus that serves as a probability density function for the position of the particle. It is natural to wonder if this stochastic characterization of the particle's position can be framed as a univariate continuous Markov process, sometimes also called a classical diffusion process, whose temporal evolution is governed by the classically transparent equations of Langevin and Fokker-Planck. (...)
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  10. Spacetime Quantum Probabilities, Relativized Descriptions, and Popperian Propensities. Part I: Spacetime Quantum Probabilities. [REVIEW]Mioara Mugur-Schächter - 1991 - Foundations of Physics 21 (12):1387-1449.
    An integrated view concerning the probabilistic organization of quantum mechanics is obtained by systematic confrontation of the Kolmogorov formulation of the abstract theory of probabilities, with the quantum mechanical representationand its factual counterparts. Because these factual counterparts possess a peculiar spacetime structure stemming from the operations by which the observer produces the studied states (operations of state preparation) and the qualifications of these (operations of measurement), the approach brings forth “probability trees,” complex constructs with treelike spacetime support.Though it is strictly (...)
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  11. Two Examples in Noncommutative Probability.Dror Bar-Natan - 1989 - Foundations of Physics 19 (1):97-104.
    A simple noncommutative probability theory is presented, and two examples for the difference between that theory and the classical theory are shown. The first example is the well-known formulation of the Heisenberg uncertainty principle in terms of a variance inequality and the second example is an interpretatio of the Bell paradox in terms of noncommuntative probability.
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  12. Selection Rules, Causality, and Unitarity in Statistical and Quantum Physics.A. Kyrala - 1974 - Foundations of Physics 4 (1):31-51.
    The integrodifferential equations satisfied by the statistical frequency functions for physical systems undergoing stochastic transitions are derived by application of a causality principle and selection rules to the Markov chain equations. The result equations can be viewed as generalizations of the diffusion equation, but, unlike the latter, they have a direct bearing onactive transport problems in biophysics andcondensation aggregation problems of astrophysics and phase transition theory. Simple specific examples of the effects of severe selection rules, such as the relaxational Boltzmann (...)
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  13. Generalized Measure Theory.Stanley Gudder - 1973 - Foundations of Physics 3 (3):399-411.
    It is argued that a reformulation of classical measure theory is necessary if the theory is to accurately describe measurements of physical phenomena. The postulates of a generalized measure theory are given and the fundamentals of this theory are developed, and the reader is introduced to some open questions and possible applications. Specifically, generalized measure spaces and integration theory are considered, the partial order structure is studied, and applications to hidden variables and the logic of quantum mechanics are given.
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  14. Martin-Löf Randomness and Galton–Watson Processes.David Diamondstone & Bjørn Kjos-Hanssen - 2012 - Annals of Pure and Applied Logic 163 (5):519-529.
  15. Essays in Mathematical Finance and in the Epistemology of Finance / Essais En Finance Mathématique Et En Epistémologie de la Finance.Xavier de Scheemaekere - unknown
    The goal of this thesis in finance is to combine the use of advanced mathematical methods with a return to foundational economic issues. In that perspective, I study generalized rational expectations and asset pricing in Chapter 2, and a converse comparison principle for backward stochastic differential equations with jumps in Chapter 3. Since the use of stochastic methods in finance is an interesting and complex issue in itself - if only to clarify the difference between the use of mathematical models (...)
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  16. A Generalisation of Bayesian Inference.Arthur Dempster - 1968 - Journal of the Royal Statistical Society Series B 30:205-247.
  17. Upper and Lower Probabilities Induced by a Multi- Valued Mapping.Arthur Dempster - 1967 - Annals of Mathematical Statistics 38:325-339.
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  18. Review of E.T. Jaynes, Probability Theory: The Logic of Science and Other Books on Probability. [REVIEW]James Franklin - 2005 - Mathematical Intelligencer 27 (2):83-85.
    Review of Jaynes, Probability Theory: The Logic of Science; Marrison, The Fundamentals of Risk Management; and Hastie, Tibshirani and Friedman, The Elements of Statistical Learning. A standard view of probability and statistics centers on distributions and hypothesis testing. To solve a real problem, say in the spread of disease, one chooses a “model”, a distribution or process that is believed from tradition or intuition to be appropriate to the class of problems in question. One uses data to estimate the parameters (...)
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  19. A Decision Procedure for Probability Calculus with Applications.Branden Fitelson - 2008 - Review of Symbolic Logic 1 (1):111-125.
    (new version: 10/30/07). Click here to download the companion Mathematica 6 notebook that goes along with this paper.
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  20. Lattice-Valued Probability.David Miller - manuscript
    A theory of probability is outlined that permits the values of the probability function to lie in any Brouwerian algebra.
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  21. Quantum Mechanics as a Theory of Probability.Itamar Pitowsky - unknown
    We develop and defend the thesis that the Hilbert space formalism of quantum mechanics is a new theory of probability. The theory, like its classical counterpart, consists of an algebra of events, and the probability measures defined on it. The construction proceeds in the following steps: (a) Axioms for the algebra of events are introduced following Birkhoff and von Neumann. All axioms, except the one that expresses the uncertainty principle, are shared with the classical event space. The only models for (...)
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Axioms of Probability
  1. Logics of Imprecise Comparative Probability.Yifeng Ding, Wesley H. Holliday & Thomas F. Icard - 2021 - International Journal of Approximate Reasoning 132:154-180.
    This paper studies connections between two alternatives to the standard probability calculus for representing and reasoning about uncertainty: imprecise probability andcomparative probability. The goal is to identify complete logics for reasoning about uncertainty in a comparative probabilistic language whose semantics is given in terms of imprecise probability. Comparative probability operators are interpreted as quantifying over a set of probability measures. Modal and dynamic operators are added for reasoning about epistemic possibility and updating sets of probability measures.
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  2. Weintraub’s Response to Williamson’s Coin Flip Argument.Matthew W. Parker - 2021 - European Journal for Philosophy of Science 11 (3):1-21.
    A probability distribution is regular if it does not assign probability zero to any possible event. Williamson argued that we should not require probabilities to be regular, for if we do, certain “isomorphic” physical events must have different probabilities, which is implausible. His remarks suggest an assumption that chances are determined by intrinsic, qualitative circumstances. Weintraub responds that Williamson’s coin flip events differ in their inclusion relations to each other, or the inclusion relations between their times, and this can account (...)
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  3. More Than Impossible: Negative and Complex Probabilities and Their Philosophical Interpretation.Vasil Penchev - 2020 - Logic and Philosophy of Mathematics eJournal (Elsevier: SSRN) 12 (16):1-7.
    A historical review and philosophical look at the introduction of “negative probability” as well as “complex probability” is suggested. The generalization of “probability” is forced by mathematical models in physical or technical disciplines. Initially, they are involved only as an auxiliary tool to complement mathematical models to the completeness to corresponding operations. Rewards, they acquire ontological status, especially in quantum mechanics and its formulation as a natural information theory as “quantum information” after the experimental confirmation the phenomena of “entanglement”. Philosophical (...)
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  4. Richard T. Cox. Probability, Frequency and Reasonable Expectation. American Journal of Physics, Vol. 14 , Pp. 1–13. - Richard T. Cox. The Algebra of Probable Inference. The Johns Hopkins Press, Baltimore1961, X + 114 Pp. [REVIEW]David Miller - 1972 - Journal of Symbolic Logic 37 (2):398-399.
  5. On Linear Aggregation of Infinitely Many Finitely Additive Probability Measures.Michael Nielsen - 2019 - Theory and Decision 86 (3-4):421-436.
    We discuss Herzberg’s :319–337, 2015) treatment of linear aggregation for profiles of infinitely many finitely additive probabilities and suggest a natural alternative to his definition of linear continuous aggregation functions. We then prove generalizations of well-known characterization results due to :410–414, 1981). We also characterize linear aggregation of probabilities in terms of a Pareto condition, de Finetti’s notion of coherence, and convexity.
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  6. Countable Additivity, Idealization, and Conceptual Realism.Yang Liu - 2020 - Economics and Philosophy 36 (1):127-147.
    This paper addresses the issue of finite versus countable additivity in Bayesian probability and decision theory -- in particular, Savage's theory of subjective expected utility and personal probability. I show that Savage's reason for not requiring countable additivity in his theory is inconclusive. The assessment leads to an analysis of various highly idealised assumptions commonly adopted in Bayesian theory, where I argue that a healthy dose of, what I call, conceptual realism is often helpful in understanding the interpretational value of (...)
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  7. Declarations of Independence.Branden Fitelson & Alan Hájek - 2017 - Synthese 194 (10):3979-3995.
    According to orthodox (Kolmogorovian) probability theory, conditional probabilities are by definition certain ratios of unconditional probabilities. As a result, orthodox conditional probabilities are undefined whenever their antecedents have zero unconditional probability. This has important ramifications for the notion of probabilistic independence. Traditionally, independence is defined in terms of unconditional probabilities (the factorization of the relevant joint unconditional probabilities). Various “equivalent” formulations of independence can be given using conditional probabilities. But these “equivalences” break down if conditional probabilities are permitted to have (...)
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  8. Bayesian Decision Theory and Stochastic Independence.Philippe Mongin - 2017 - TARK 2017.
    Stochastic independence has a complex status in probability theory. It is not part of the definition of a probability measure, but it is nonetheless an essential property for the mathematical development of this theory. Bayesian decision theorists such as Savage can be criticized for being silent about stochastic independence. From their current preference axioms, they can derive no more than the definitional properties of a probability measure. In a new framework of twofold uncertainty, we introduce preference axioms that entail not (...)
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  9. Inferring Probability Comparisons.Matthew Harrison-Trainor, Wesley H. Holliday & Thomas Icard - 2018 - Mathematical Social Sciences 91:62-70.
    The problem of inferring probability comparisons between events from an initial set of comparisons arises in several contexts, ranging from decision theory to artificial intelligence to formal semantics. In this paper, we treat the problem as follows: beginning with a binary relation ≥ on events that does not preclude a probabilistic interpretation, in the sense that ≥ has extensions that are probabilistically representable, we characterize the extension ≥+ of ≥ that is exactly the intersection of all probabilistically representable extensions of (...)
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  10. 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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  11. On the Structure of the Quantum-Mechanical Probability Models.Nicola Cufaro-Petroni - 1992 - Foundations of Physics 22 (11):1379-1401.
    In this paper the role of the mathematical probability models in the classical and quantum physics is shortly analyzed. In particular the formal structure of the quantum probability spaces (QPS) is contrasted with the usual Kolmogorovian models of probability by putting in evidence the connections between this structure and the fundamental principles of the quantum mechanics. The fact that there is no unique Kolmogorovian model reproducing a QPS is recognized as one of the main reasons of the paradoxical behaviors pointed (...)
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  12. Maximum Likelihood Estimation on Generalized Sample Spaces: An Alternative Resolution of Simpson's Paradox. [REVIEW]Matthias P. Kläy & David J. Foulis - 1990 - Foundations of Physics 20 (7):777-799.
    We propose an alternative resolution of Simpson's paradox in multiple classification experiments, using a different maximum likelihood estimator. In the center of our analysis is a formal representation of free choice and randomization that is based on the notion of incompatible measurements.We first introduce a representation of incompatible measurements as a collection of sets of outcomes. This leads to a natural generalization of Kolmogoroff's axioms of probability. We then discuss the existence and uniqueness of the maximum likelihood estimator for a (...)
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  13. On Probability Theory and Probabilistic Physics—Axiomatics and Methodology.L. S. Mayants - 1973 - Foundations of Physics 3 (4):413-433.
    A new formulation involving fulfillment of all the Kolmogorov axioms is suggested for acomplete probability theory. This proves to be not a purely mathematical discipline. Probability theory deals with abstract objects—images of various classes of concrete objects—whereas experimental statistics deals with concrete objects alone. Both have to be taken into account. Quantum physics and classical statistical physics prove to be different aspects ofone probabilistic physics. The connection of quantum mechanics with classical statistical mechanics is examined and the origin of the (...)
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  14. Proof of Kolmogorovian Censorship.Gergely Bana & Thomas Durt - 1997 - Foundations of Physics 27 (10):1355-1373.
    Many argued that Kolmogorov's axioms of classical probability theory are incompatible with quantum probabilities, and that this is the reason for the violation of Bell's inequalities. Szabó showed that, in fact, these inequalities are not violated by the experimentally observed frequencies if we consider the real, “effective” frequencies. We prove in this work a theorem which generalizes this results: “effective” frequencies associated to quantum events always admit a Kolmogorovian representation, when these events are collected through different experimental setups, the choice (...)
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  15. Axioms for Non-Archimedean Probability (NAP).Vieri Benci, Leon Horsten & Sylvia Wenmackers - 2012 - In De Vuyst J. & Demey L. (eds.), Future Directions for Logic; Proceedings of PhDs in Logic III - Vol. 2 of IfColog Proceedings. College Publications.
    In this contribution, we focus on probabilistic problems with a denumerably or non-denumerably infinite number of possible outcomes. Kolmogorov (1933) provided an axiomatic basis for probability theory, presented as a part of measure theory, which is a branch of standard analysis or calculus. Since standard analysis does not allow for non-Archimedean quantities (i.e. infinitesimals), we may call Kolmogorov's approach "Archimedean probability theory". We show that allowing non-Archimedean probability values may have considerable epistemological advantages in the infinite case. The current paper (...)
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  16. On the Impossibility of Events of Zero Probability.Asad Zaman - 1987 - Theory and Decision 23 (2):157-159.
  17. Varieties of Conditional Probability.Kenny Easwaran - 2011 - In Prasanta Bandyopadhyay & Malcolm Forster (eds.), Handbook of the Philosophy of Science, Vol. 7: Philosophy of Statistics. North Holland.
    I consider the notions of logical probability, degree of belief, and objective chance, and argue that a different formalism for conditional probability is appropriate for each.
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  18. Non-Archimedean Probability.Vieri Benci, Leon Horsten & Sylvia Wenmackers - 2013 - Milan Journal of Mathematics 81 (1):121-151.
    We propose an alternative approach to probability theory closely related to the framework of numerosity theory: non-Archimedean probability (NAP). In our approach, unlike in classical probability theory, all subsets of an infinite sample space are measurable and only the empty set gets assigned probability zero (in other words: the probability functions are regular). We use a non-Archimedean field as the range of the probability function. As a result, the property of countable additivity in Kolmogorov’s axiomatization of probability is replaced by (...)
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  19. A Note on Comparative Probability.Nick Haverkamp & Moritz Schulz - 2012 - Erkenntnis 76 (3):395-402.
    A possible event always seems to be more probable than an impossible event. Although this constraint, usually alluded to as regularity , is prima facie very attractive, it cannot hold for standard probabilities. Moreover, in a recent paper Timothy Williamson has challenged even the idea that regularity can be integrated into a comparative conception of probability by showing that the standard comparative axioms conflict with certain cases if regularity is assumed. In this note, we suggest that there is a natural (...)
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  20. Probabilistic Coherence and Proper Scoring Rules.Joel Predd, Robert Seiringer, Elliott Lieb, Daniel Osherson, H. Vincent Poor & Sanjeev Kulkarni - 2009 - IEEE Transactions on Information Theory 55 (10):4786-4792.
    We provide self-contained proof of a theorem relating probabilistic coherence of forecasts to their non-domination by rival forecasts with respect to any proper scoring rule. The theorem recapitulates insights achieved by other investigators, and clarifi es the connection of coherence and proper scoring rules to Bregman divergence.
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  21. Probabilité Conditionnelle Et Certitude.Bas C. Van Fraassen - 1997 - Dialogue 36 (1):69-.
    Personal probability is now a familiar subject in epistemology, together with such more venerable notions as knowledge and belief. But there are severe strains between probability and belief; if either is taken as the more basic, the other may suffer. After explaining the difficulties of attempts to accommodate both, I shall propose a unified account which takes conditional personal probability as basic. Full belief is therefore a defined, derivative notion. Yet we will still be able to picture opinion as follows: (...)
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  22. On Generalizing Kolmogorov.Richard Dietz - 2010 - Notre Dame Journal of Formal Logic 51 (3):323-335.
    In his "From classical to constructive probability," Weatherson offers a generalization of Kolmogorov's axioms of classical probability that is neutral regarding the logic for the object-language. Weatherson's generalized notion of probability can hardly be regarded as adequate, as the example of supervaluationist logic shows. At least, if we model credences as betting rates, the Dutch-Book argument strategy does not support Weatherson's notion of supervaluationist probability, but various alternatives. Depending on whether supervaluationist bets are specified as (a) conditional bets (Cantwell), (b) (...)
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  23. Degrees of Belief.Franz Huber & Christoph Schmidt-Petri (eds.) - 2008 - Dordrecht and Heidelberg: Springer.
    Various theories try to give accounts of how measures of this confidence do or ought to behave, both as far as the internal mental consistency of the agent as ...
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  24. Pragmatic Probability.Newton C. A. Costa - 1986 - Erkenntnis 25 (2):141-162.
  25. Likelihood.Anthony William Fairbank Edwards - 1972 - Cambridge University Press.
    Dr Edwards' stimulating and provocative book advances the thesis that the appropriate axiomatic basis for inductive inference is not that of probability, with its addition axiom, but rather likelihood - the concept introduced by Fisher as a measure of relative support amongst different hypotheses. Starting from the simplest considerations and assuming no more than a modest acquaintance with probability theory, the author sets out to reconstruct nothing less than a consistent theory of statistical inference in science.
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  26. Probability Semantics for Quantifier Logic.Theodore Hailperin - 2000 - Journal of Philosophical Logic 29 (2):207-239.
    By supplying propositional calculus with a probability semantics we showed, in our 1996, that finite stochastic problems can be treated by logic-theoretic means equally as well as by the usual set-theoretic ones. In the present paper we continue the investigation to further the use of logical notions in probability theory. It is shown that quantifier logic, when supplied with a probability semantics, is capable of treating stochastic problems involving countably many trials.
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  27. The Lockean Thesis and the Logic of Belief.James Hawthorne - 2009 - In Franz Huber & Christoph Schmidt-Petri (eds.), Degrees of Belief. Synthese Library: Springer. pp. 49--74.
    In a penetrating investigation of the relationship between belief and quantitative degrees of confidence (or degrees of belief) Richard Foley (1992) suggests the following thesis: ... it is epistemically rational for us to believe a proposition just in case it is epistemically rational for us to have a sufficiently high degree of confidence in it, sufficiently high to make our attitude towards it one of belief. Foley goes on to suggest that rational belief may be just rational degree of confidence (...)
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  28. Getting the Constraints on Popper's Probability Functions Right.Hugues Leblanc & Peter Roeper - 1993 - Philosophy of Science 60 (1):151-157.
    Shown here is that a constraint used by Popper in The Logic of Scientific Discovery (1959) for calculating the absolute probability of a universal quantification, and one introduced by Stalnaker in "Probability and Conditionals" (1970, 70) for calculating the relative probability of a negation, are too weak for the job. The constraint wanted in the first case is in Bendall (1979) and that wanted in the second case is in Popper (1959).
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  29. Probability Functions and Their Assumption Sets — the Binary Case.Hugues Leblanc & Charles G. Morgan - 1984 - Synthese 60 (1):91 - 106.
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