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  1. Mikel Aickin (2000). Connecting Dempster–Shafer Belief Functions with Likelihood-Based Inference. 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. Salem Benferhat, Alessandro Saffiotti & Philippe Smets (2000). Belief Functions and Default Reasoning. Artificial Intelligence 122 (1--2):1--69.
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  3. Arthur Dempster (2008). The Dempster-Shafer Calculus for Statisticians. International Journal of Approximate Reasoning 48 (2):365--377.
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  4. Thierry Denoeux (2008). Conjunctive and Disjunctive Combination of Belief Functions Induced by Nondistinct Bodies of Evidence. Artificial Intelligence 172 (2--3):234--264.
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  5. Didier Dubois (1986). Belief Structures, Possibility Theory and Decomposable Confidence Measures on Finite Sets. Computers and Artificial Intelligence (5):403--416.
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  6. Didier Dubois, Petr Hájek & Henri Prade (2000). Knowledge-Driven Versus Data-Driven Logics. 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 rules as (...)
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  7. Rolf Haenni & Stephan Hartmann (2006). Modeling Partially Reliable Information Sources: A General Approach Based on Dempster-Shafer Theory. Information Fusion 7:361-379.
    Combining testimonial reports from independent and partially reliable information sources is an important 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, 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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  8. Joseph Y. Halpern (2003). Reasoning About Uncertainty. Mit Press.
  9. Joseph Y. Halpern & Riccardo Pucella (2007). Characterizing and Reasoning About Probabilistic and Non-Probabilistic Expectation. J. Acm 54 (3):15.
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  10. David Harmanec, George Klir & Zhenyuan Wang (1996). Modal Logic Interpretation of Dempster-Shafer Theory: An Infinite Case. International Journal of Approximate Reasoning 14 (2--3):81--93.
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  11. Stephan Hartmann & Rolf Haenni, Modeling Partially Reliable Information Sources: A General Approach Based on Dempster-Shafer Theory.
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  12. Franz Huber, Formal Representations of Belief. 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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  13. J. Y. Jaffray (1994). Decision Making with Belief Functions. In R. Yager, M. Fedrizzi & J. Kacprzyk (eds.), Advances in the Dempster- Shafer Theory of Evidence. John Wiley. 331-352.
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  14. Jan Komorowski, Lech T. Polkowski & Andrzej Skowron (1997). Towards a Rough Mereology-Based Logic for Approximate Solution Synthesis. Part. 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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  15. Kyburg Jr, E. Henry & Michael Pittarelli (1996). Set-Based Bayesianism. Ieee Transactions on Systems, Man and Cybernetics A 26 (3):324--339.
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  16. Henry E. Kyburg (1992). Getting Fancy with Probability. 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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  17. E. Kyburg, Henry (1987). Bayesian and Non-Bayesian Evidential Updating. Artificial Intelligence 31:271--294.
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  18. Kyburg, Jr & E. Henry (1987). Bayesian and Non-Bayesian Evidence and Updating. Artificial Intelligence 31:271-293.
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  19. Weiru Liu (2006). Analyzing the Degree of Conflict Among Belief Functions. Artificial Intelligence 170 (11):909--924.
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  20. Judea Pearl (1992). Rejoinder to Comments on ``Reasoning with Belief Functions: An Analysis of Compatibility. International Journal of Approximate Reasoning 6 (3):425--443.
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  21. Judea Pearl (1990). Reasoning with Belief Functions: An Analysis of Compatibility. International Journal of Approximate Reasoning 4:363--389.
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  22. Judea Pearl (1988). Probabilistic Reasoning in Intelligent Systems. 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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  23. Jan-Willem Romeijn (2012). Conditioning and Interpretation Shifts. 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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  24. Glenn Shafer (2010). A Betting Interpretation for Probabilities and Dempster-Shafer Degrees of Belief. International Journal of Approximate Reasoning.
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  25. Glenn Shafer (1976). A Mathematical Theory of Evidence. Princeton University Press.
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  26. Phillippe Smets (1997). The Normative Representation of Quantified Beliefs by Belief Functions. Artificial Intelligence 92 (1--2):229--242.
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  27. Phillippe Smets (1993). Belief Functions: The Disjunctive Rule of Combination and the Generalized Bayesian Theorem. International Journal of Approximate Reasoning 9 (1):1--35.
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  28. Phillippe Smets (1992). Resolving Misunderstandings About Belief Functions. International Journal of Approximate Reasoning 6 (3):321--344.
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  29. Elena Tsiporkova, Vesilka Boeva & Bernard De Baets (1999). Dempster-Shafer Theory Framed in Modal Logic. International Journal of Approximate Reasoning 21 (2):157--175.
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  30. Larry Wasserman (1992). Comments on Shafer's``Perspectives on the Theory and Practice of Belief Functions''. International Journal of Approximate Reasoning 6 (2):367--375.
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  31. Jonathan Weisberg, Dempster-Shafer Theory.
    An introduction to Dempster-Shafter Theory, from a lecture at the Northern Institute of Philosophy in 2010.
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  32. R. Yager, M. Fedrizzi & J. Kacprzyk (eds.) (1994). Advances in the Dempster- Shafer Theory of Evidence. John Wiley.
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  33. Ronald R. Yager (2009). Comparing Approximate Reasoning and Probabilistic Reasoning Using the Dempster-Shafer Framework. International Journal of Approximate Reasoning 50 (5):812--821.
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  34. Ronald R. Yager (1987). On the Dempster-Shafer Framework and New Combination Rules. Information Sciences 41 (2):93--137.
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  35. Boutheina Ben Yaghlane, Phillippe Smets & Khaled Mellouli (2002). Belief Function Independence: II. The Conditional Case. International Journal of Approximate Reasoning 31 (1--2):31--75.
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  36. Boutheina Ben Yaghlane, Phillippe Smets & Khaled Mellouli (2002). Belief Function Independence: I. The Marginal Case. International Journal of Approximate Reasoning 29 (1):47--70.
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  37. Elia Zardini (2012). Luminosity and Vagueness. 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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