Recently, different semantics for relational probabilistic conditionals and corresponding maximum entropy (ME) inference operators have been proposed. In this paper, we study the so-called aggregation semantics that covers both notions of a statistical and subjective view. The computation of its inference operator requires the calculation of the ME-distribution satisfying all probabilistic conditionals, inducing an optimization problem under linear constraints. We demonstrate how the well-known Generalized Iterative Scaling (GIS) algorithm technique can be applied to this optimization problem and present a practical algorithm and its implementation.
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