Concepts and Algorithms for Computing Maximum Entropy Distributions for Knowledge Bases with Relational Probabilistic Conditionals

Author:

Finthammer MarcORCID

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence

Reference12 articles.

1. Beierle C, Kern-Isberner G, Finthammer M, Potyka N (2015) Extending and completing probabilistic knowledge and beliefs without bias. KI Künstliche Intelligenz 29(3):255–262

2. Beierle C, Krämer A (2015) Achieving parametric uniformity for knowledge bases in a relational probabilistic conditional logic with maximum entropy semantics. Ann Math Artif Intell 73:1–2

3. Darroch JN, Ratcliff D (1972) Generalized iterative scaling for log-linear models. In: Annals of Mathematical Statistics, vol 43. Institute of Mathematical Statistics, pp 1470–1480

4. Finthammer M (2017) Concepts and algorithms for computing maximum entropy distributions for knowledge bases with relational probabilistic conditionals. Dissertations in artificial intelligence. IOS Press

5. Finthammer M, Beierle C (2016) On the relationship between aggregating semantics and FO-PCL grounding semantics for relational probabilistic conditionals. In: Beierle C, Brewka G, Thimm M (eds) Computational models of rationality. Essays Dedicated to Gabriele Kern-Isberner on the Occasion of her 60th Birthday, Tributes, vol 29. College Publications, pp 297–315

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