BLOG

BLOG: probabilistic models with unknown objects. This paper introduces and illustrates BLOG, a formal language for defining probability models over worlds with unknown objects and identity uncertainty. BLOG unifies and extends several existing approaches. Subject to certain acyclicity constraints, every BLOG model specifies a unique probability distribution over first-order model structures that can contain varying and unbounded numbers of objects. Furthermore, complete inference algorithms exist for a large fragment of the language. We also introduce a probabilistic form of Skolemization for handling evidence.

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References in zbMATH (referenced in 41 articles )

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  1. Belle, Vaishak; De Raedt, Luc: Semiring programming: a semantic framework for generalized sum product problems (2020)
  2. Belle, Vaishak; Levesque, Hector J.: Regression and progression in stochastic domains (2020)
  3. Henderson, T. C.; Simmons, R.; Serbinowski, B.; Cline, M.; Sacharny, D.; Fan, X.; Mitiche, A.: Probabilistic sentence satisfiability: an approach to PSAT (2020)
  4. Mauá, Denis Deratani; Cozman, Fabio Gagliardi: Thirty years of credal networks: specification, algorithms and complexity (2020)
  5. Cozman, Fabio Gagliardi; Mauá, Denis Deratani: The finite model theory of Bayesian network specifications: descriptive complexity and zero/one laws (2019)
  6. Sridharan, Mohan; Gelfond, Michael; Zhang, Shiqi; Wyatt, Jeremy: REBA: a refinement-based architecture for knowledge representation and reasoning in robotics (2019)
  7. Belle, Vaishak; Levesque, Hector J.: Reasoning about discrete and continuous noisy sensors and effectors in dynamical systems (2018)
  8. Cozman, Fabio G.; Mauá, Denis D.: The complexity of Bayesian networks specified by propositional and relational languages (2018)
  9. Bach, Stephen H.; Broecheler, Matthias; Huang, Bert; Getoor, Lise: Hinge-loss Markov random fields and probabilistic soft logic (2017)
  10. Carvalho, Rommel N.; Laskey, Kathryn B.; Costa, Paulo C. G.: PR-OWL - a language for defining probabilistic ontologies (2017)
  11. Nitti, Davide; Belle, Vaishak; De Laet, Tinne; De Raedt, Luc: Planning in hybrid relational MDPs (2017)
  12. Orsini, Francesco; Frasconi, Paolo; De Raedt, Luc: kProbLog: an algebraic Prolog for machine learning (2017)
  13. Kiselyov, Oleg: Probabilistic programming language and its incremental evaluation (2016)
  14. Nitti, Davide; De Laet, Tinne; De Raedt, Luc: Probabilistic logic programming for hybrid relational domains (2016)
  15. Riguzzi, Fabrizio: The distribution semantics for normal programs with function symbols (2016)
  16. Vlasselaer, Jonas; Van den Broeck, Guy; Kimmig, Angelika; Meert, Wannes; De Raedt, Luc: (T_\mathcalP)-compilation for inference in probabilistic logic programs (2016)
  17. Wingate, David; Kane, Jonathan; Wolinsky, Matt; Sylvester, Zoltán: A new approach for conditioning process-based geologic models to well data (2016)
  18. Belle, Vaishak; Levesque, Hector J.: Robot location estimation in the situation calculus (2015)
  19. De Raedt, Luc; Kimmig, Angelika: Probabilistic (logic) programming concepts (2015)
  20. Hadiji, Fabian; Molina, Alejandro; Natarajan, Sriraam; Kersting, Kristian: Poisson dependency networks: gradient boosted models for multivariate count data (2015)

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