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Stewart, Rush T. and Quintana, Ignacio Ojea (2018): Learning and Pooling, Pooling and Learning. In: Erkenntnis, Vol. 83: pp. 369-389 [PDF, 372kB]

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We explore which types of probabilistic updating commute with convex IP pooling (Stewart and Ojea Quintana 2017). Positive results are stated for Bayesian conditionalization (and a mild generalization of it), imaging, and a certain parameterization of Jeffrey conditioning. This last observation is obtained with the help of a slight generalization of a characterization of (precise) externally Bayesian pooling operators due to Wagner (Log J IGPL 18(2):336--345, 2009). These results strengthen the case that pooling should go by imprecise probabilities since no precise pooling method is as versatile.

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