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  1.  61
    Probabilistic Logics and Probabilistic Networks.Rolf Haenni, Jan-Willem Romeijn, Gregory Wheeler & Jon Williamson - 2011 - Synthese Library.
    Additionally, the text shows how to develop computationally feasible methods to mesh with this framework.
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  2. 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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  3.  86
    Possible Semantics for a Common Framework of Probabilistic Logics.Gregory Wheeler, Jon Williamson, Jan-Willem Romeijn & Rolf Haenni - 2008 - In V. N. Huynh (ed.), International Workshop on Interval Probabilistic Uncertainty and Non-Classical Logics. Springer.
    Summary. This paper proposes a common framework for various probabilistic logics. It consists of a set of uncertain premises with probabilities attached to them. This raises the question of the strength of a conclusion, but without imposing a particular semantics, no general solution is possible. The paper discusses several possible semantics by looking at it from the perspective of probabilistic argumentation.
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  4.  5
    Combining Probability and Logic.Fabio Cozman, Rolf Haenni, Jan-Willem Romeijn, Federica Russo, Gregory Wheeler & Jon Williamson - 2009 - Journal of Applied Logic 7 (2):131-135.
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  5.  5
    Probabilistic Argumentation.Rolf Haenni - 2009 - Journal of Applied Logic 7 (2):155-176.
  6.  15
    Special Issue of Minds and Machines on Causality, Uncertainty and Ignorance.Stephan Hartmann & Rolf Haenni - 2006 - Minds and Machines 16 (3):237-238.
  7. Logical Relations in a Statistical Problem.Jon Williamson, Jan-Willem Romeijn, Rolf Haenni & Gregory Wheeler - 2008 - In Benedikt Lowe, Jan-Willem Romeijn & Eric Pacuit (eds.), Proceedings of the Foundations of the Formal Sciences VI: Reasoning about probabilities and probabilistic reasoning. College Publications.
    This paper presents the progicnet programme. It proposes a general framework for probabilistic logic that can guide inference based on both logical and probabilistic input. After an introduction to the framework as such, it is illustrated by means of a toy example from psychometrics. It is shown that the framework can accommodate a number of approaches to probabilistic reasoning: Bayesian statistical inference, evidential probability, probabilistic argumentation, and objective Bayesianism. The framework thus provides insight into the relations between these approaches, it (...)
     
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  8.  6
    Non-Additive Degrees of Belief.Rolf Haenni - 2009 - In Franz Huber & Christoph Schmidt-Petri (eds.), Degrees of Belief. Springer. pp. 121--159.
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  9.  28
    Causality, Uncertainty and Ignorance.Rolf Haenni & Stephan Hartmann - 2006 - Minds and Machines 16 (3).
    Special issue. With contributions by Malcolm Forster, Rocio Garcia-Rotamero and Ulrich Hoffrage, Christian Jakob, Kevin Korb and Erik Nyberg, Michael Smithson, Daniel Steel, Brad Weslake, and Jon Williamson.
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