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  1. Connecting Dempster–Shafer Belief Functions with Likelihood-Based Inference.Mikel Aickin - 2000 - Synthese 123 (3):347-364.
    The Dempster–Shafer approach to expressing beliefabout a parameter in a statistical model is notconsistent with the likelihood principle. Thisinconsistency has been recognized for some time, andmanifests itself as a non-commutativity, in which theorder of operations (combining belief, combininglikelihood) makes a difference. It is proposed herethat requiring the expression of belief to be committed to the model (and to certain of itssubmodels) makes likelihood inference very nearly aspecial case of the Dempster–Shafer theory.
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  2. Belief Functions and Default Reasoning.Salem Benferhat, Alessandro Saffiotti & Philippe Smets - 2000 - Artificial Intelligence 122 (1--2):1--69.
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  3. The Dempster-Shafer Calculus for Statisticians.Arthur Dempster - 2008 - International Journal of Approximate Reasoning 48 (2):365--377.
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  4. Conjunctive and Disjunctive Combination of Belief Functions Induced by Nondistinct Bodies of Evidence.Thierry Denoeux - 2008 - Artificial Intelligence 172 (2--3):234--264.
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  5. Belief Structures, Possibility Theory and Decomposable Confidence Measures on Finite Sets.Didier Dubois - 1986 - Computers and Artificial Intelligence (5):403--416.
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  6. Knowledge-Driven Versus Data-Driven Logics.Didier Dubois, Petr Hájek & Henri Prade - 2000 - Journal of Logic, Language and Information 9 (1):65--89.
    The starting point of this work is the gap between two distinct traditions in information engineering: knowledge representation and data - driven modelling. The first tradition emphasizes logic as a tool for representing beliefs held by an agent. The second tradition claims that the main source of knowledge is made of observed data, and generally does not use logic as a modelling tool. However, the emergence of fuzzy logic has blurred the boundaries between these two traditions by putting forward fuzzy (...)
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  7. Reasoning About Uncertainty.Joseph Y. Halpern - 2003 - MIT Press.
  8. Characterizing and Reasoning About Probabilistic and Non-Probabilistic Expectation.Joseph Y. Halpern & Riccardo Pucella - 2007 - J. Acm 54 (3):15.
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  9. Modal Logic Interpretation of Dempster-Shafer Theory: An Infinite Case.David Harmanec, George Klir & Zhenyuan Wang - 1996 - International Journal of Approximate Reasoning 14 (2--3):81--93.
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  10. Modeling Partially Reliable Information Sources: A General Approach Based on Dempster-Shafer Theory.Stephan Hartmann & Rolf Haenni - 2006 - Information Fusion 7:361-379.
    Combining testimonial reports from independent and partially reliable information sources is an important epistemological problem of uncertain reasoning. Within the framework of Dempster–Shafer theory, we propose a general model of partially reliable sources, which includes several previously known results as special cases. The paper reproduces these results on the basis of a comprehensive model taxonomy. This gives a number of new insights and thereby contributes to a better understanding of this important application of reasoning with uncertain and incomplete information.
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  11. Formal Representations of Belief.Franz Huber - 2008 - Stanford Encyclopedia of Philosophy.
    Epistemology is the study of knowledge and justified belief. Belief is thus central to epistemology. It comes in a qualitative form, as when Sophia believes that Vienna is the capital of Austria, and a quantitative form, as when Sophia's degree of belief that Vienna is the capital of Austria is at least twice her degree of belief that tomorrow it will be sunny in Vienna. Formal epistemology, as opposed to mainstream epistemology (Hendricks 2006), is epistemology done in a formal way, (...)
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  12. Decision Making with Belief Functions.J. Y. Jaffray - 1994 - In R. Yager, M. Fedrizzi & J. Kacprzyk (eds.), Advances in the Dempster- Shafer Theory of Evidence. John Wiley. pp. 331-352.
  13. Towards a Rough Mereology-Based Logic for Approximate Solution Synthesis. Part.Jan Komorowski, Lech T. Polkowski & Andrzej Skowron - 1997 - Studia Logica 58 (1):143-184.
    We are concerned with formal models of reasoning under uncertainty. Many approaches to this problem are known in the literature e.g. Dempster-Shafer theory [29], [42], bayesian-based reasoning [21], [29], belief networks [29], many-valued logics and fuzzy logics [6], non-monotonic logics [29], neural network logics [14]. We propose rough mereology developed by the last two authors [22-25] as a foundation for approximate reasoning about complex objects. Our notion of a complex object includes, among others, proofs understood as schemes constructed in order (...)
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  14. Set-Based Bayesianism. Kyburg Jr, E. Henry & Michael Pittarelli - 1996 - Ieee Transactions on Systems, Man and Cybernetics A 26 (3):324--339.
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  15. Getting Fancy with Probability.Henry E. Kyburg - 1992 - Synthese 90 (2):189-203.
    There are a number of reasons for being interested in uncertainty, and there are also a number of uncertainty formalisms. These formalisms are not unrelated. It is argued that they can all be reflected as special cases of the approach of taking probabilities to be determined by sets of probability functions defined on an algebra of statements. Thus, interval probabilities should be construed as maximum and minimum probabilities within a set of distributions, Glenn Shafer's belief functions should be construed as (...)
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  16. Bayesian and Non-Bayesian Evidential Updating.E. Kyburg, Henry - 1987 - Artificial Intelligence 31:271--294.
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  17. Bayesian and Non-Bayesian Evidence and Updating. Kyburg, Jr & E. Henry - 1987 - Artificial Intelligence 31:271-293.
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  18. Analyzing the Degree of Conflict Among Belief Functions.Weiru Liu - 2006 - Artificial Intelligence 170 (11):909--924.
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  19. Rejoinder to Comments on ``Reasoning with Belief Functions: An Analysis of Compatibility.Judea Pearl - 1992 - International Journal of Approximate Reasoning 6 (3):425--443.
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  20. Reasoning with Belief Functions: An Analysis of Compatibility.Judea Pearl - 1990 - International Journal of Approximate Reasoning 4:363--389.
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  21. Probabilistic Reasoning in Intelligent Systems.Judea Pearl - 1988 - Morgan Kaufmann.
    The book can also be used as an excellent text for graduate-level courses in AI, operations research, or applied probability.
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  22. Conditioning and Interpretation Shifts.Jan-Willem Romeijn - 2012 - Studia Logica 100 (3):583-606.
    This paper develops a probabilistic model of belief change under interpretation shifts, in the context of a problem case from dynamic epistemic logic. Van Benthem [4] has shown that a particular kind of belief change, typical for dynamic epistemic logic, cannot be modelled by standard Bayesian conditioning. I argue that the problems described by van Benthem come about because the belief change alters the semantics in which the change is supposed to be modelled: the new information induces a shift in (...)
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  23. A Betting Interpretation for Probabilities and Dempster-Shafer Degrees of Belief.Glenn Shafer - 2010 - International Journal of Approximate Reasoning.
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  24. A Mathematical Theory of Evidence.Glenn Shafer - 1976 - Princeton University Press.
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  25. The Normative Representation of Quantified Beliefs by Belief Functions.Phillippe Smets - 1997 - Artificial Intelligence 92 (1--2):229--242.
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  26. Belief Functions: The Disjunctive Rule of Combination and the Generalized Bayesian Theorem.Phillippe Smets - 1993 - International Journal of Approximate Reasoning 9 (1):1--35.
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  27. Resolving Misunderstandings About Belief Functions.Phillippe Smets - 1992 - International Journal of Approximate Reasoning 6 (3):321--344.
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  28. Dempster-Shafer Theory Framed in Modal Logic.Elena Tsiporkova, Vesilka Boeva & Bernard De Baets - 1999 - International Journal of Approximate Reasoning 21 (2):157--175.
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  29. Evidence Theory in Multivalued Models of Modal Logic.Elena Tsiporkova, Bernard De Baets & Veselka Boeva - 2000 - Journal of Applied Non-Classical Logics 10 (1):55-81.
    ABSTRACT A modal logic interpretation of Dempster-Shafer theory is developed in the framework of multivalued models of modal logic, i.e. models in which in any possible world an arbitrary number (possibly zero) of atomic propositions can be true. Several approaches to conditioning in multivalued models of modal logic are presented.
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  30. Comments on Shafer's``Perspectives on the Theory and Practice of Belief Functions''.Larry Wasserman - 1992 - International Journal of Approximate Reasoning 6 (2):367--375.
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  31. Dempster-Shafer Theory.Jonathan Weisberg - 2010
    An introduction to Dempster-Shafter Theory, from a lecture at the Northern Institute of Philosophy in 2010.
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  32. Advances in the Dempster- Shafer Theory of Evidence.R. Yager, M. Fedrizzi & J. Kacprzyk (eds.) - 1994 - John Wiley.
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  33. Comparing Approximate Reasoning and Probabilistic Reasoning Using the Dempster-Shafer Framework.Ronald R. Yager - 2009 - International Journal of Approximate Reasoning 50 (5):812--821.
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  34. On the Dempster-Shafer Framework and New Combination Rules.Ronald R. Yager - 1987 - Information Sciences 41 (2):93--137.
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  35. Belief Function Independence: I. The Marginal Case.Boutheina Ben Yaghlane, Phillippe Smets & Khaled Mellouli - 2002 - International Journal of Approximate Reasoning 29 (1):47--70.
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  36. Belief Function Independence: II. The Conditional Case.Boutheina Ben Yaghlane, Phillippe Smets & Khaled Mellouli - 2002 - International Journal of Approximate Reasoning 31 (1--2):31--75.
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  37. Luminosity and Vagueness.Elia Zardini - 2012 - Dialectica 66 (3):375-410.
    The paper discusses some ways in which vagueness and its phenomena may be thought to impose certain limits on our knowledge and, more specifically, may be thought to bear on the traditional philosophical idea that certain domains of facts are luminous, i.e., roughly, fully open to our view. The discussion focuses on a very influential argument to the effect that almost no such interesting domains exist. Many commentators have felt that the vagueness unavoidably inherent in the description of the facts (...)
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