PRISM

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.


References in zbMATH (referenced in 33 articles )

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  1. Balai, Evgenii; Gelfond, Michael; Zhang, Yuanlin: P-log: refinement and a new coherency condition (2019)
  2. Nguembang Fadja, Arnaud; Riguzzi, Fabrizio: Lifted discriminative learning of probabilistic logic programs (2019)
  3. Abdallah, Samer: PRISM revisited: declarative implementation of a probabilistic programming language using multi-prompt delimited control (2018)
  4. Bain, Michael; Srinivasan, Ashwin: Identification of biological transition systems using meta-interpreted logic programs (2018)
  5. Karabatsos, George; Leisen, Fabrizio: An approximate likelihood perspective on ABC methods (2018)
  6. Angelopoulos, Nicos; Cussens, James: Distributional logic programming for Bayesian knowledge representation (2017)
  7. Buchman, David; Poole, David: Negative probabilities in probabilistic logic programs (2017)
  8. Orsini, Francesco; Frasconi, Paolo; De Raedt, Luc: kProbLog: an algebraic prolog for machine learning (2017)
  9. 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)
  10. Riguzzi, Fabrizio; Cota, Giuseppe; Bellodi, Elena; Zese, Riccardo: Causal inference in cplint (2017)
  11. Zhang, Lianyi; Lo, Kueiming; Qing, Duzheng; Wang, Weijing; Yu, Lixin: Statistical model checking of stochastic component-based systems (2017)
  12. Nampally, Arun; Ramakrishnan, C. R.: Inference in probabilistic logic programs using lifted explanations (2016)
  13. Nickles, Matthias: A tool for probabilistic reasoning based on logic programming and first-order theories under stable model semantics (2016)
  14. Orsini, Francesco; Frasconi, Paolo; De Raedt, Luc: kProbLog: an algebraic Prolog for kernel programming (2016)
  15. Alsanie, Waleed; Cussens, James: Learning failure-free PRISM programs (2015)
  16. De Raedt, Luc; Kimmig, Angelika: Probabilistic (logic) programming concepts (2015)
  17. Michels, Steffen; Hommersom, Arjen; Lucas, Peter J. F.; Velikova, Marina: A new probabilistic constraint logic programming language based on a generalised distribution semantics (2015)
  18. Van Ranst, Wiebe; Vennekens, Joost: An OpenCL implementation of a forward sampling algorithm for CP-logic (2015)
  19. Howard, Catherine; Stumptner, Markus: A survey of directed entity-relation-based first-order probabilistic languages (2014)
  20. Chiang, Michael; Poole, David: Reference classes and relational learning (2012)

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