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. Srinivasan, Ashwin; Vig, Lovekesh; Shroff, Gautam: Constructing generative logical models for optimisation problems using domain knowledge (2020)
  2. Balai, Evgenii; Gelfond, Michael; Zhang, Yuanlin: P-log: refinement and a new coherency condition (2019)
  3. Nguembang Fadja, Arnaud; Riguzzi, Fabrizio: Lifted discriminative learning of probabilistic logic programs (2019)
  4. Abdallah, Samer: PRISM revisited: declarative implementation of a probabilistic programming language using multi-prompt delimited control (2018)
  5. Bain, Michael; Srinivasan, Ashwin: Identification of biological transition systems using meta-interpreted logic programs (2018)
  6. Karabatsos, George; Leisen, Fabrizio: An approximate likelihood perspective on ABC methods (2018)
  7. Angelopoulos, Nicos; Cussens, James: Distributional logic programming for Bayesian knowledge representation (2017)
  8. Buchman, David; Poole, David: Negative probabilities in probabilistic logic programs (2017)
  9. Orsini, Francesco; Frasconi, Paolo; De Raedt, Luc: kProbLog: an algebraic Prolog for machine learning (2017)
  10. 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)
  11. Riguzzi, Fabrizio; Cota, Giuseppe; Bellodi, Elena; Zese, Riccardo: Causal inference in cplint (2017)
  12. Zhang, Lianyi; Lo, Kueiming; Qing, Duzheng; Wang, Weijing; Yu, Lixin: Statistical model checking of stochastic component-based systems (2017)
  13. Nampally, Arun; Ramakrishnan, C. R.: Inference in probabilistic logic programs using lifted explanations (2016)
  14. Nickles, Matthias: A tool for probabilistic reasoning based on logic programming and first-order theories under stable model semantics (2016)
  15. Orsini, Francesco; Frasconi, Paolo; De Raedt, Luc: kProbLog: an algebraic Prolog for kernel programming (2016)
  16. Alsanie, Waleed; Cussens, James: Learning failure-free PRISM programs (2015)
  17. De Raedt, Luc; Kimmig, Angelika: Probabilistic (logic) programming concepts (2015)
  18. Michels, Steffen; Hommersom, Arjen; Lucas, Peter J. F.; Velikova, Marina: A new probabilistic constraint logic programming language based on a generalised distribution semantics (2015)
  19. Van Ranst, Wiebe; Vennekens, Joost: An OpenCL implementation of a forward sampling algorithm for CP-logic (2015)
  20. Howard, Catherine; Stumptner, Markus: A survey of directed entity-relation-based first-order probabilistic languages (2014)

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