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  1. Sets of probability distributions, independence, and convexity.Fabio G. Cozman - 2012 - Synthese 186 (2):577-600.
    This paper analyzes concepts of independence and assumptions of convexity in the theory of sets of probability distributions. The starting point is Kyburg and Pittarelli’s discussion of “convex Bayesianism” (in particular their proposals concerning E-admissibility, independence, and convexity). The paper offers an organized review of the literature on independence for sets of probability distributions; new results on graphoid properties and on the justification of “strong independence” (using exchangeability) are presented. Finally, the connection between Kyburg and Pittarelli’s results and recent developments (...)
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  • Quasi-Bayesian Analysis Using Imprecise Probability Assessments And The Generalized Bayes' Rule.Kathleen M. Whitcomb - 2005 - Theory and Decision 58 (2):209-238.
    The generalized Bayes’ rule (GBR) can be used to conduct ‘quasi-Bayesian’ analyses when prior beliefs are represented by imprecise probability models. We describe a procedure for deriving coherent imprecise probability models when the event space consists of a finite set of mutually exclusive and exhaustive events. The procedure is based on Walley’s theory of upper and lower prevision and employs simple linear programming models. We then describe how these models can be updated using Cozman’s linear programming formulation of the GBR. (...)
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  • Robust Bayes classifiers.Marco Ramoni & Paola Sebastiani - 2001 - Artificial Intelligence 125 (1-2):209-226.
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  • On the complexity of solving polytree-shaped limited memory influence diagrams with binary variables.Denis Deratani Mauá, Cassio Polpo de Campos & Marco Zaffalon - 2013 - Artificial Intelligence 205 (C):30-38.
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  • Updating beliefs with incomplete observations.Gert de Cooman & Marco Zaffalon - 2004 - Artificial Intelligence 159 (1-2):75-125.
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  • Credal networks.Fabio G. Cozman - 2000 - Artificial Intelligence 120 (2):199-233.
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