Tabular: A Schema-driven Probabilistic Programming Language
| Autor: | John Guiver, Johannes Borgström, Claudio V. Russo, Nicolas Rolland, Andrew D. Gordon, Thore Graepel |
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| Jazyk: | angličtina |
| Rok vydání: | 2014 |
| Předmět: |
Relational database
Computer science Programming language Computer Sciences Formal semantics (linguistics) probabilistic programming Statistical model Notation Bayesian inference Missing data computer.software_genre Computer Graphics and Computer-Aided Design model-learner pattern Datavetenskap (datalogi) machine learning Ranking Semantics of logic Schema (psychology) relational data Probabilistic programming language bayesian reasoning Cluster analysis Bayesian reasoning computer Software |
| Zdroj: | Gordon, A D, Graepel, T, Rolland, N, Russo, C, Borgstrom, J & Guiver, J 2014, Tabular: A Schema-driven Probabilistic Programming Language . in Proceedings of the 41st ACM SIGPLAN-SIGACT Symposium on Principles of Programming Languages . ACM SIGPLAN Notices, no. 1, vol. 49, New York, NY, USA, pp. 321-334, 41st ACM SIGPLAN-SIGACT Symposium on Principles of Programming Languages, San Diego, California, United States, 22/01/14 . https://doi.org/10.1145/2535838.2535850 POPL |
| Popis: | We propose a new kind of probabilistic programming language for machine learning. We write programs simply by annotating existing relational schemas with probabilistic model expressions. We describe a detailed design of our language, Tabular, complete with formal semantics and type system. A rich series of examples illustrates the expressiveness of Tabular. We report an implementation, and show evidence of the succinctness of our notation relative to current best practice. Finally, we describe and verify a transformation of Tabular schemas so as to predict missing values in a concrete database. The ability to query for missing values provides a uniform interface to a wide variety of tasks, including classification, clustering, recommendation, and ranking. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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