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.
Keywords for this software
References in zbMATH (referenced in 39 articles )
Showing results 1 to 20 of 39.
Sorted by year (- Azzolini, Damiano; Bellodi, Elena; Ferilli, Stefano; Riguzzi, Fabrizio; Zese, Riccardo: Abduction with probabilistic logic programming under the distribution semantics (2022)
- Doleschal, Johannes; Kimelfeld, Benny; Martens, Wim; Peterfreund, Liat: Weight annotation in information extraction (2022)
- Fraccaroli, Michele; Lamma, Evelina; Riguzzi, Fabrizio: Symbolic DNN-tuner (2022)
- Riguzzi, Fabrizio; Bellodi, Elena; Zese, Riccardo; Alberti, Marco; Lamma, Evelina: Probabilistic inductive constraint logic (2021)
- Srinivasan, Ashwin; Vig, Lovekesh; Shroff, Gautam: Constructing generative logical models for optimisation problems using domain knowledge (2020)
- Balai, Evgenii; Gelfond, Michael; Zhang, Yuanlin: P-log: refinement and a new coherency condition (2019)
- Ghosh, Sarthak; Ramakrishnan, C. R.: Value of information in probabilistic logic programs (2019)
- Nguembang Fadja, Arnaud; Riguzzi, Fabrizio: Lifted discriminative learning of probabilistic logic programs (2019)
- Abdallah, Samer: PRISM revisited: declarative implementation of a probabilistic programming language using multi-prompt delimited control (2018)
- Bain, Michael; Srinivasan, Ashwin: Identification of biological transition systems using meta-interpreted logic programs (2018)
- Karabatsos, George; Leisen, Fabrizio: An approximate likelihood perspective on ABC methods (2018)
- Angelopoulos, Nicos; Cussens, James: Distributional logic programming for Bayesian knowledge representation (2017)
- Buchman, David; Poole, David: Negative probabilities in probabilistic logic programs (2017)
- Orsini, Francesco; Frasconi, Paolo; De Raedt, Luc: kProbLog: an algebraic Prolog for machine learning (2017)
- 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)
- Riguzzi, Fabrizio; Cota, Giuseppe; Bellodi, Elena; Zese, Riccardo: Causal inference in cplint (2017)
- Zhang, Lianyi; Lo, Kueiming; Qing, Duzheng; Wang, Weijing; Yu, Lixin: Statistical model checking of stochastic component-based systems (2017)
- Nampally, Arun; Ramakrishnan, C. R.: Inference in probabilistic logic programs using lifted explanations (2016)
- Nickles, Matthias: A tool for probabilistic reasoning based on logic programming and first-order theories under stable model semantics (2016)
- Orsini, Francesco; Frasconi, Paolo; De Raedt, Luc: kProbLog: an algebraic Prolog for kernel programming (2016)