Search results for 'Probabilities' (try it on Scholar)

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  1. Horacio Arló-Costa & Jeffrey Helzner (2010). Ambiguity Aversion: The Explanatory Power of Indeterminate Probabilities. Synthese 172 (1):37 - 55.score: 24.0
    Daniel Ellsberg presented in Ellsberg (The Quarterly Journal of Economics 75:643–669, 1961) various examples questioning the thesis that decision making under uncertainty can be reduced to decision making under risk. These examples constitute one of the main challenges to the received view on the foundations of decision theory offered by Leonard Savage in Savage (1972). Craig Fox and Amos Tversky have, nevertheless, offered an indirect defense of Savage. They provided in Fox and Tversky (1995) an explanation of Ellsberg’s two-color problem (...)
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  2. Alan Hájek & Michael Smithson (2012). Rationality and Indeterminate Probabilities. Synthese 187 (1):33-48.score: 24.0
    We argue that indeterminate probabilities are not only rationally permissible for a Bayesian agent, but they may even be rationally required . Our first argument begins by assuming a version of interpretivism: your mental state is the set of probability and utility functions that rationalize your behavioral dispositions as well as possible. This set may consist of multiple probability functions. Then according to interpretivism, this makes it the case that your credal state is indeterminate. Our second argument begins with (...)
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  3. Igor Douven & Jos Uffink (2012). Quantum Probabilities and the Conjunction Principle. Synthese 184 (1):109-114.score: 24.0
    A recent argument by Hawthorne and Lasonen-Aarnio purports to show that we can uphold the principle that competently forming conjunctions is a knowledge-preserving operation only at the cost of a rampant skepticism about the future. A key premise of their argument is that, in light of quantum-mechanical considerations, future contingents never quite have chance 1 of being true. We argue, by drawing attention to the order of magnitude of the relevant quantum probabilities, that the skeptical threat of Hawthorne and (...)
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  4. Jarosław Pykacz (2006). “Solution” of the EPR Paradox: Negative, or Rather Fuzzy Probabilities? [REVIEW] Foundations of Physics 36 (3):437-442.score: 24.0
    Negative probabilities were several times proposed in the literature as a way to reconcile violation of Bell-type inequalities with the premise of local realism. It is argued that instead of using negative probabilities that have no physical meaning one can use for this purpose fuzzy probabilities that have sound and unambiguous interpretation.
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  5. Peter Baumann (2008). Single-Case Probabilities and the Case of Monty Hall: Levy's View. Synthese 162 (2):265 - 273.score: 24.0
    In Baumann (American Philosophical Quarterly 42: 71–79, 2005) I argued that reflections on a variation of the Monty Hall problem throws a very general skeptical light on the idea of single-case probabilities. Levy (Synthese, forthcoming, 2007) puts forward some interesting objections which I answer here.
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  6. Andrei Y. Khrennikov & Elena R. Loubenets (2004). On Relations Between Probabilities Under Quantum and Classical Measurements. Foundations of Physics 34 (4):689-704.score: 24.0
    We show that the so-called quantum probabilistic rule, usually introduced in the physical literature as an argument of the essential distinction between the probability relations under quantum and classical measurements, is not, as it is commonly accepted, in contrast to the rule for the addition of probabilities of mutually exclusive events. The latter is valid under all experimental situations upon classical and quantum systems. We discuss also the quantum measurement situation that is similar to the classical one, described by (...)
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  7. John M. Myers (2006). Conditional Probabilities and Density Operators in Quantum Modeling. Foundations of Physics 36 (7):1012-1035.score: 24.0
    Motivated by a recent proof of free choices in linking equations to the experiments they describe, I clarify some relations among purely mathematical entities featured in quantum mechanics (probabilities, density operators, partial traces, and operator-valued measures), thereby allowing applications of these entities to the modeling of a wider variety of physical situations. I relate conditional probabilities associated with projection-valued measures to conditional density operators identical, in some cases but not in others, to the usual reduced density operators. While (...)
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  8. Robert B. Griffiths (2003). Probabilities and Quantum Reality: Are There Correlata? [REVIEW] Foundations of Physics 33 (10):1423-1459.score: 24.0
    Any attempt to introduce probabilities into quantum mechanics faces difficulties due to the mathematical structure of Hilbert space, as reflected in Birkhoff and von Neumann's proposal for a quantum logic. The (consistent or decoherent) histories solution is provided by its single framework rule, an approach that includes conventional (Copenhagen) quantum theory as a special case. Mermin's Ithaca interpretation addresses the same problem by defining probabilities which make no reference to a sample space or event algebra (“correlations without correlata”). (...)
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  9. Matthew R. Kelley & Robert J. Lemke (forthcoming). Gender Differences When Subjective Probabilities Affect Risky Decisions: An Analysis From the Television Game Show Cash Cab. [REVIEW] Theory and Decision:1-18.score: 24.0
    This study uses the television show Cash Cab as a natural experiment to investigate gender differences in decision making under uncertainty. As expected, men are much more likely to accept the end-of-game gamble than are women, but men and women appear to weigh performance variables differently when relying on subjective probabilities. At best men base their risky decisions on general aspects of their previous “good” play (not all of which is relevant at the time the decision is made) and (...)
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  10. Mohamed A. Amer (1985). Extension of Relatively |Sigma-Additive Probabilities on Boolean Algebras of Logic. Journal of Symbolic Logic 50 (3):589 - 596.score: 24.0
    Contrary to what is stated in Lemma 7.1 of [8], it is shown that some Boolean algebras of finitary logic admit finitely additive probabilities that are not σ-additive. Consequences of Lemma 7.1 are reconsidered. The concept of a C-σ-additive probability on B (where B and C are Boolean algebras, and $\mathscr{B} \subseteq \mathscr{C}$ ) is introduced, and a generalization of Hahn's extension theorem is proved. This and other results are employed to show that every S̄(L)-σ-additive probability on s̄(L) can (...)
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  11. Lyle C. Gurrin, Peter D. Sly & Paul R. Burton (2002). Using Imprecise Probabilities to Address the Questions of Inference and Decision in Randomized Clinical Trials. Journal of Evaluation in Clinical Practice 8 (2):255-268.score: 24.0
    Randomized controlled clinical trials play an important role in the development of new medical therapies. There is, however, an ethical issue surrounding the use of randomized treatment allocation when the patient is suffering from a life threatening condition and requires immediate treatment. Such patients can only benefit from the treatment they actually receive and not from the alternative therapy, even if it ultimately proves to be superior. We discuss a novel new way to analyse data from such clinical trials based (...)
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  12. Christopher Schwand, Rudolf Vetschera & Lea M. Wakolbinger (2010). The Influence of Probabilities on the Response Mode Bias in Utility Elicitation. Theory and Decision 69 (3):395-416.score: 24.0
    The response mode bias, in which subjects exhibit different risk attitudes when assessing certainty equivalents versus indifference probabilities, is a well-known phenomenon in the assessment of utility functions. In this empirical study, we develop and apply a cardinal measure of risk attitudes to analyze not only the existence, but also the strength of this phenomenon. Since probability levels involved in decision problems are already known to have a strong impact on behavior, we use this approach to study the impact (...)
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  13. Hugues Leblanc (1962/2006). Statistical and Inductive Probabilities. Dover Publications.score: 24.0
    This evenhanded treatment addresses the decades-old dispute among probability theorists, asserting that both statistical and inductive probabilities may be treated as sentence-theoretic measurements, and that the latter qualify as estimates of the former. Beginning with a survey of the essentials of sentence theory and of set theory, the author examines statistical probabilities, showing that statistical probabilities may be passed on to sentences, and thereby qualify as truth-values. An exploration of inductive probabilities follows, demonstrating their reinterpretation as (...)
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  14. Bas van Fraassen (1976). Probabilities of Conditionals. In C. Hooker (ed.), Foundations of probability theory, statistical inference, and statistical theories of science.score: 22.0
  15. Jake Chandler (forthcoming). Subjective Probabilities Need Not Be Sharp. Erkenntnis:1-14.score: 22.0
    It is well known that classical, aka ‘sharp’, Bayesian decision theory, which models belief states as single probability functions, faces a number of serious difficulties with respect to its handling of agnosticism. These difficulties have led to the increasing popularity of so-called ‘imprecise’ models of decision-making, which represent belief states as sets of probability functions. In a recent paper, however, Adam Elga has argued in favour of a putative normative principle of sequential choice that he claims to be borne out (...)
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  16. Jordan Howard Sobel (2009). Modus Ponens and Modus Tollens for Conditional Probabilities, and Updating on Uncertain Evidence. Theory and Decision 66 (2):103 - 148.score: 22.0
    There are narrowest bounds for P(h) when P(e) = y and P(h/e) = x, which bounds collapse to x as y goes to 1. A theorem for these bounds -- bounds for probable modus ponens -- entails a principle for updating on possibly uncertain evidence subject to these bounds that is a generalization of the principle for updating by conditioning on certain evidence. This way of updating on possibly uncertain evidence is appropriate when updating by ’probability kinematics’ or ’Jeffrey-conditioning’ is, (...)
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  17. Giuseppe Attanasi & Aldo Montesano (2012). The Price for Information About Probabilities and its Relation with Risk and Ambiguity. Theory and Decision 73 (1):125-160.score: 22.0
    In this article, ambiguity attitude is measured through the maximum price a decision maker is willing to pay to know the probability of an event. Two problems are examined in which the decision maker faces an act: in one case, buying information implies playing a lottery, while, in the other case, buying information gives also the option to avoid playing the lottery. In both decision settings, relying on the Choquet expected utility model, we study how the decision maker’s risk and (...)
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  18. Ebbe Groes, Hans Jørgen Jacobsen, Birgitte Sloth & Torben Tranaes (1998). Nash Equilibrium with Lower Probabilities. Theory and Decision 44 (1):37-66.score: 22.0
    We generalize the concept of Nash equilibrium in mixed strategies for strategic form games to allow for ambiguity in the players' expectations. In contrast to other contributions, we model ambiguity by means of so-called lower probability measures or belief functions, which makes it possible to distinguish between a player's assessment of ambiguity and his attitude towards ambiguity. We also generalize the concept of trembling hand perfect equilibrium. Finally, we demonstrate that for certain attitudes towards ambiguity it is possible to explain (...)
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  19. Rob Lawlor (2006). Taurek, Numbers and Probabilities. Ethical Theory and Moral Practice 9 (2):149 - 166.score: 21.0
    In his paper, “Should the Numbers Count?" John Taurek imagines that we are in a position such that we can either save a group of five people, or we can save one individual, David. We cannot save David and the five. This is because they each require a life-saving drug. However, David needs all of the drug if he is to survive, while the other five need only a fifth each.Typically, people have argued as if there was a choice to (...)
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  20. Wayne C. Myrvold (forthcoming). Probabilities in Statistical Mechanics. In Christopher Hitchcock & Alan Hájek (eds.), The Oxford Handbook of Probability and Philosophy. Oxford University Press.score: 21.0
    This chapter will review selected aspects of the terrain of discussions about probabilities in statistical mechanics (with no pretensions to exhaustiveness, though the major issues will be touched upon), and will argue for a number of claims. None of the claims to be defended is entirely original, but all deserve emphasis. The first, and least controversial, is that probabilistic notions are needed to make sense of statistical mechanics. The reason for this is the same reason that convinced Maxwell, Gibbs, (...)
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  21. David Atkinson & Jeanne Peijnenburg (2009). Justification by an Infinity of Conditional Probabilities. Notre Dame Journal of Formal Logic 50 (2):183-193.score: 21.0
    Today it is generally assumed that epistemic justification comes in degrees. The consequences, however, have not been adequately appreciated. In this paper we show that the assumption invalidates some venerable attacks on infinitism: once we accept that epistemic justification is gradual, an infinitist stance makes perfect sense. It is only without the assumption that infinitism runs into difficulties.
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  22. Andrei Khrennikov (2005). The Principle of Supplementarity: A Contextual Probabilistic Viewpoint to Complementarity, the Interference of Probabilities and Incompatibility of Variables in Quantum Mechanics. Foundations of Physics 35 (10):1655-1693.score: 21.0
  23. Edi Karni & Zvi Safra (1995). The Impossibility of Experimental Elicitation of Subjective Probabilities. Theory and Decision 38 (3):313-320.score: 21.0
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  24. Paul Slovic & Sarah Lichtenstein (1968). Relative Importance of Probabilities and Payoffs in Risk Taking. Journal of Experimental Psychology 78 (3p2):1.score: 21.0
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  25. M. S. Mayzner & M. E. Tresselt (1962). Anagram Solution Times: A Function of Word Transition Probabilities. Journal of Experimental Psychology 63 (5):510.score: 21.0
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  26. Ebbe Groes, Hans Jørgen Jacobsen, Birgitte Sloth & Torben Tranaes (1998). Nash Equilibrium with Lower Probabilities. Theory and Decision 44 (1):37-66.score: 21.0
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  27. Philip Brickman & Scott M. Pierce (1972). Estimates of Conditional Probabilities of Confirming Versus Disconfirming Events as a Function of Inference Situation and Prior Evidence. Journal of Experimental Psychology 95 (1):235.score: 21.0
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  28. Marvin H. Detambel (1956). Probabilities of Success and Amounts of Work in a Multichoice Situation. Journal of Experimental Psychology 51 (1):41.score: 21.0
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  29. Gloria J. Fischer (1971). Number of Event Choices and the Difference Between Event Probabilities in Human Probability Learning. Journal of Experimental Psychology 89 (1):192.score: 21.0
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  30. Cameron R. Peterson, Z. J. Ulehla, Alan J. Miller, Lyle E. Bourne Jr & Donald W. Stilson (1965). Internal Consistency of Subjective Probabilities. Journal of Experimental Psychology 70 (5):526.score: 21.0
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  31. Cameron R. Peterson, Robert J. Schneider & Alan J. Miller (1965). Sample Size and the Revision of Subjective Probabilities. Journal of Experimental Psychology 69 (5):522.score: 21.0
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  32. Lee R. Beach (1968). Probability Magnitudes and Conservative Revision of Subjective Probabilities. Journal of Experimental Psychology 77 (1):57.score: 21.0
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  33. John G. Borkowski & Gilbert R. Johns (1966). Analysis of Learning Rate and Sampling Probabilities in a Contingent Reinforcement Situation. Journal of Experimental Psychology 72 (1):158.score: 21.0
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  34. Jean Engler (1958). Marginal and Conditional Stimulus and Response Probabilities in Verbal Conditioning. Journal of Experimental Psychology 55 (4):303.score: 21.0
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  35. William C. Howell & Joseph F. Funaro (1965). Prediction on the Basis of Conditional Probabilities. Journal of Experimental Psychology 69 (1):92.score: 21.0
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  36. Jerome L. Myers & Donna Cruse (1968). Two-Choice Discrimination Learning as a Function of Stimulus and Event Probabilities. Journal of Experimental Psychology 77 (3p1):453.score: 21.0
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  37. Maynard W. Shelly Ii (1958). The Effects of Response Contingent Probabilities Which Favor Response Change. Journal of Experimental Psychology 56 (3):239.score: 21.0
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  38. Sheila G. Zipf (1961). The Effects of Amount of Reward, Requirement, and Several Related Probabilities on Human Performance. Journal of Experimental Psychology 62 (5):503.score: 21.0
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  39. Igor Douven (2008). Kaufmann on the Probabilities of Conditionals. Journal of Philosophical Logic 37 (3):259 - 266.score: 20.0
    Kaufmann has recently argued that the thesis according to which the probability of an indicative conditional equals the conditional probability of the consequent given the antecedent under certain specifiable circumstances deviates from intuition. He presents a method for calculating the probability of a conditional that does seem to give the intuitively correct result under those circumstances. However, the present paper shows that Kaufmann’s method is inconsistent in that it may lead one to assign different probabilities to a single conditional (...)
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  40. Johan van Benthem, Jelle Gerbrandy & Barteld Kooi (2009). Dynamic Update with Probabilities. Studia Logica 93 (1):67-96.score: 20.0
    Current dynamic-epistemic logics model different types of information change in multi-agent scenarios. We generalize these logics to a probabilistic setting, obtaining a calculus for multi-agent update with three natural slots: prior probability on states, occurrence probabilities in the relevant process taking place, and observation probabilities of events. To match this update mechanism, we present a complete dynamic logic of information change with a probabilistic character. The completeness proof follows a compositional methodology that applies to a much larger class (...)
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  41. Katharina Rasmussen (2012). Should the Probabilities Count? Philosophical Studies 159 (2):205-218.score: 20.0
    When facing a choice between saving one person and saving many, some people have argued that fairness requires us to decide without aggregating numbers; rather we should decide by coin toss or some form of lottery, or alternatively we should straightforwardly save the greater number but justify this in a non-aggregating contractualist way. This paper expands the debate beyond well-known number cases to previously under-considered probability cases, in which not (only) the numbers of people, but (also) the probabilities of (...)
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  42. Richard Bradley (2006). Adams Conditionals and Non-Monotonic Probabilities. Journal of Logic, Language and Information 15 (1-2):65-81.score: 20.0
    Adams' famous thesis that the probabilities of conditionals are conditional probabilities is incompatible with standard probability theory. Indeed it is incompatible with any system of monotonic conditional probability satisfying the usual multiplication rule for conditional probabilities. This paper explores the possibility of accommodating Adams' thesis in systems of non-monotonic probability of varying strength. It shows that such systems impose many familiar lattice theoretic properties on their models as well as yielding interesting logics of conditionals, but that a (...)
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  43. Ken Levy (2007). Baumann on the Monty Hall Problem and Single-Case Probabilities. Synthese 158 (1):139 - 151.score: 20.0
    Peter Baumann uses the Monty Hall game to demonstrate that probabilities cannot be meaningfully applied to individual games. Baumann draws from this first conclusion a second: in a single game, it is not necessarily rational to switch from the door that I have initially chosen to the door that Monty Hall did not open. After challenging Baumann’s particular arguments for these conclusions, I argue that there is a deeper problem with his position: it rests on the false assumption that (...)
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  44. Jiji Zhang (2008). Error Probabilities for Inference of Causal Directions. Synthese 163 (3):409 - 418.score: 20.0
    A main message from the causal modelling literature in the last several decades is that under some plausible assumptions, there can be statistically consistent procedures for inferring (features of) the causal structure of a set of random variables from observational data. But whether we can control the error probabilities with a finite sample size depends on the kind of consistency the procedures can achieve. It has been shown that in general, under the standard causal Markov and Faithfulness assumptions, the (...)
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  45. Sven Ove Hansson (2010). Past Probabilities. Notre Dame Journal of Formal Logic 51 (2):207-223.score: 20.0
    The probability that a fair coin tossed yesterday landed heads is either 0 or 1, but the probability that it would land heads was 0.5. In order to account for the latter type of probabilities, past probabilities, a temporal restriction operator is introduced and axiomatically characterized. It is used to construct a representation of conditional past probabilities. The logic of past probabilities turns out to be strictly weaker than the logic of standard probabilities.
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  46. Jacob Rosenthal (2012). Probabilities as Ratios of Ranges in Initial-State Spaces. Journal of Logic, Language and Information 21 (2):217-236.score: 20.0
    A proposal for an objective interpretation of probability is introduced and discussed: probabilities as deriving from ranges in suitably structured initial-state spaces. Roughly, the probability of an event on a chance trial is the proportion of initial states that lead to the event in question within the space of all possible initial states associated with this type of experiment, provided that the proportion is approximately the same in any not too small subregion of the space. This I would like (...)
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  47. John Pollock (2011). Reasoning Defeasibly About Probabilities. Synthese 181 (2):317 - 352.score: 20.0
    In concrete applications of probability, statistical investigation gives us knowledge of some probabilities, but we generally want to know many others that are not directly revealed by our data. For instance, we may know prob(P/Q) (the probability of P given Q) and prob(P/R), but what we really want is prob(P/Q& R), and we may not have the data required to assess that directly. The probability calculus is of no help here. Given prob(P/Q) and prob(P/R), it is consistent with the (...)
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  48. Gerd Niestegge (2008). An Approach to Quantum Mechanics Via Conditional Probabilities. Foundations of Physics 38 (3):241-256.score: 20.0
    The well-known proposal to consider the Lüders-von Neumann measurement as a non-classical extension of probability conditionalization is further developed. The major results include some new concepts like the different grades of compatibility, the objective conditional probabilities which are independent of the underlying state and stem from a certain purely algebraic relation between the events, and an axiomatic approach to quantum mechanics. The main axioms are certain postulates concerning the conditional probabilities and own intrinsic probabilistic interpretations from the very (...)
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  49. Peter Milne (2008). Bets and Boundaries: Assigning Probabilities to Imprecisely Specified Events. Studia Logica 90 (3):425 - 453.score: 20.0
    Uncertainty and vagueness/imprecision are not the same: one can be certain about events described using vague predicates and about imprecisely specified events, just as one can be uncertain about precisely specified events. Exactly because of this, a question arises about how one ought to assign probabilities to imprecisely specified events in the case when no possible available evidence will eradicate the imprecision (because, say, of the limits of accuracy of a measuring device). Modelling imprecision by rough sets over an (...)
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  50. Todd A. Oliynyk (2005). Hidden Measurements, Hidden Variables and the Volume Representation of Transition Probabilities. Foundations of Physics 35 (1):85-107.score: 20.0
    We construct, for any finite dimension n, a new hidden measurement model for quantum mechanics based on representing quantum transition probabilities by the volume of regions in projective Hilbert space. For n=2 our model is equivalent to the Aerts sphere model and serves as a generalization of it for dimensions n .≥ 3 We also show how to construct a hidden variables scheme based on hidden measurements and we discuss how joint distributions arise in our hidden variables scheme and (...)
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