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  1. Rolf Haenni, Jan-Willem Romeijn, Gregory Wheeler & Jon Williamson (2011). Probabilistic Logics and Probabilistic Networks. Synthese Library.
    Additionally, the text shows how to develop computationally feasible methods to mesh with this framework.
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  2. Rolf Haenni (2009). Non-Additive Degrees of Belief. In. In Franz Huber & Christoph Schmidt-Petri (eds.), Degrees of Belief. Springer. 121--159.
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  3. Jan-Willem Romeijn, Jon Williamson, Gregory Wheeler & Rolf Haenni (2008). Possible Semantics for a Common Framework of Probabilistic Logics. In V. N. Huynh (ed.), International Workshop on Interval Probabilistic Uncertainty and Non-Classical Logics. Springer.
    In V. N. Huynh (ed.): Interval / Probabilistic Uncertainty and Non-Classical Logics, Advances in Soft Computing Series, Springer 2008, pp. 268-279. 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. Gregory Wheeler, Jon Williamson, Jan-Willem Romeijn & Rolf Haenni (2008). Possible Semantics for a Common Framework of Probabilistic Logics. 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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  5. Jon Williamson, Jan-Willem Romeijn, Rolf Haenni & Gregory Wheeler (2008). Logical Relations in a Statistical Problem. 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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  6. 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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  7. Rolf Haenni & Stephan Hartmann (2006). Causality, Uncertainty and Ignorance. 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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  8. Stephan Hartmann & Rolf Haenni, Modeling Partially Reliable Information Sources: A General Approach Based on Dempster-Shafer Theory.
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  9. Stephan Hartmann & Rolf Haenni (2006). Special Issue of Minds and Machines on Causality, Uncertainty and Ignorance. Minds and Machines 16 (3):237-238.
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