Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
The uncertain reasoner's companion: a mathematical perspective
The uncertain reasoner's companion: a mathematical perspective
A maximum entropy approach to natural language processing
Computational Linguistics
On first-order conditional logics
Artificial Intelligence
Conditional logic and the principle of entropy
Artificial Intelligence
An implementation of the iterative proportional fitting procedure by propagation trees
Computational Statistics & Data Analysis
Convex Optimization
Combining probabilistic logic programming with the power of maximum entropy
Artificial Intelligence - Special issue on nonmonotonic reasoning
Conditionals in nonmonotonic reasoning and belief revision: considering conditionals as agents
Conditionals in nonmonotonic reasoning and belief revision: considering conditionals as agents
Markov Logic: An Interface Layer for Artificial Intelligence
Markov Logic: An Interface Layer for Artificial Intelligence
Coherent knowledge processing at maximum entropy by spirit
UAI'96 Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence
Automated reasoning for relational probabilistic knowledge representation
IJCAR'10 Proceedings of the 5th international conference on Automated Reasoning
JELIA'12 Proceedings of the 13th European conference on Logics in Artificial Intelligence
Using equivalences of worlds for aggregation semantics of relational conditionals
KI'12 Proceedings of the 35th Annual German conference on Advances in Artificial Intelligence
Transactions on Large-Scale Data- and Knowledge-Centered Systems VI
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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.