PRISM: A language for symbolic-statistical modeling. We present an overview of symbolic-statistical modeling language PRISM whose programs are not only a probabilistic extension of logic programs but also able to learn from examples with the help of the EM learning algorithm. As a knowledge representation language appropriate for probabilistic reasoning, it can describe various types of symbolic-statistical modeling formalism known but unrelated so far in a single framework. We show by examples, together with learning results, that most popular probabilistic modeling formalisms, the hidden Markov model and Bayesian networks, are described by PRISM programs.

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  1. Azzolini, Damiano; Bellodi, Elena; Ferilli, Stefano; Riguzzi, Fabrizio; Zese, Riccardo: Abduction with probabilistic logic programming under the distribution semantics (2022)
  2. Doleschal, Johannes; Kimelfeld, Benny; Martens, Wim; Peterfreund, Liat: Weight annotation in information extraction (2022)
  3. Fraccaroli, Michele; Lamma, Evelina; Riguzzi, Fabrizio: Symbolic DNN-tuner (2022)
  4. Riguzzi, Fabrizio; Bellodi, Elena; Zese, Riccardo; Alberti, Marco; Lamma, Evelina: Probabilistic inductive constraint logic (2021)
  5. Srinivasan, Ashwin; Vig, Lovekesh; Shroff, Gautam: Constructing generative logical models for optimisation problems using domain knowledge (2020)
  6. Balai, Evgenii; Gelfond, Michael; Zhang, Yuanlin: P-log: refinement and a new coherency condition (2019)
  7. Ghosh, Sarthak; Ramakrishnan, C. R.: Value of information in probabilistic logic programs (2019)
  8. Nguembang Fadja, Arnaud; Riguzzi, Fabrizio: Lifted discriminative learning of probabilistic logic programs (2019)
  9. Abdallah, Samer: PRISM revisited: declarative implementation of a probabilistic programming language using multi-prompt delimited control (2018)
  10. Bain, Michael; Srinivasan, Ashwin: Identification of biological transition systems using meta-interpreted logic programs (2018)
  11. Karabatsos, George; Leisen, Fabrizio: An approximate likelihood perspective on ABC methods (2018)
  12. Angelopoulos, Nicos; Cussens, James: Distributional logic programming for Bayesian knowledge representation (2017)
  13. Buchman, David; Poole, David: Negative probabilities in probabilistic logic programs (2017)
  14. Orsini, Francesco; Frasconi, Paolo; De Raedt, Luc: kProbLog: an algebraic Prolog for machine learning (2017)
  15. Riguzzi, Fabrizio; Bellodi, Elena; Zese, Riccardo; Cota, Giuseppe; Lamma, Evelina: A survey of lifted inference approaches for probabilistic logic programming under the distribution semantics (2017)
  16. Riguzzi, Fabrizio; Cota, Giuseppe; Bellodi, Elena; Zese, Riccardo: Causal inference in cplint (2017)
  17. Zhang, Lianyi; Lo, Kueiming; Qing, Duzheng; Wang, Weijing; Yu, Lixin: Statistical model checking of stochastic component-based systems (2017)
  18. Nampally, Arun; Ramakrishnan, C. R.: Inference in probabilistic logic programs using lifted explanations (2016)
  19. Nickles, Matthias: A tool for probabilistic reasoning based on logic programming and first-order theories under stable model semantics (2016)
  20. Orsini, Francesco; Frasconi, Paolo; De Raedt, Luc: kProbLog: an algebraic Prolog for kernel programming (2016)

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