PRISM: A Language for Symbolic-Statistical Modeling

By Taisuke Sato, Yoshitaka Kameya. 1997

Subfields: Probabilistic & Bayesian Methods

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Citations in the corpus

An Introduction to Probabilistic Programming (2018, background): “, 2011),(Sato and Kameya, 1997) – and sophisticated, useful, and widely deployed libraries/embedded domain-specific languages for modeling and inference (Infer.”
Venture: a higher-order probabilistic programming platform with programmable inference (2014, background): “Additionally, a number of probabilistic extensions of classical logic programming languages have also been developed (Poole, 1997; Sato and Kameya, 1997; De Raedt and Kersting, 2008), motivated by problems in statistical relational learning.”
FACTORIE: Probabilistic Programming via Imperatively Defined Factor Graphs (2009, methodology): “Regarding logic, for many years there has been considerable effort in integrating first-order logic and probability [9, 10, 11, 12, 13].”
Parameter Learning of Logic Programs for Symbolic-Statistical Modeling (2001, methodology): “We have been developing a symbolic-statistical modeling laguage PRISM since 1995 (URL = http://mi.cs.titech.ac.jp/prism/) as an implementation of distribution semantics (Sato, 1995; Sato & Kameya, 1997; Sato, 1998).”
Towards Integrative Machine Learning and Knowledge Extraction (2017, methodology): “For example, [50] introduces an approach for localizing vertebras using a combined segmentation and localization approach.”
Uniform Coherence (2016, background): “For more on this subject, refer to [22], [23], [24], [25], [26], [27], [28], [29].”
Probabilistic (logic) programming concepts (2015, methodology): “While early versions of PRISM (Sato, 1995; Sato and Kameya, 1997) used binary or n-ary probabilistic choices with an argument that explicitly distinguished between diﬀerent calls, this argument has been made implicit later on (Sato and Kameya, 2001), meaning that the PRISM implementation never…”