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14 - Tabular: Probabilistic Inference from the Spreadsheet

Published online by Cambridge University Press:  18 November 2020

By
Andrew D. Gordon ,
Claudio Russo ,
Marcin Szymczak ,
Johannes Borgström ,
Nicolas Rolland ,
Thore Graepel  and
Daniel Tarlow
Edited by
Gilles Barthe ,
Joost-Pieter Katoen  and
Alexandra Silva
Gilles Barthe
Affiliation:
Max Planck Institute for Security and Privacy
Joost-Pieter Katoen
Affiliation:
RWTH Aachen University, Germany
Alexandra Silva
Affiliation:
University College London
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Summary

Tabular is a domain-specific language for expressing probabilistic models of relational data. Tabular has several features that set it apart from other probabilistic programming languages including: (1) programs and data are stored as spreadsheet tables; (2) programs consist of probabilistic annotations on the relational schema of the data; and (3) inference returns estimations of missing values and latent columns, as well as parameters. Our primary implementation is for Microsoft Excel and relies on Infer.NET for inference. Still, the language can be called independently of Excel and can target alternative inference engines.

Keywords

Language designoperational semanticsdependent typesspreadsheets
Type
Chapter
Information
Foundations of Probabilistic Programming , pp. 489 - 532
Publisher: Cambridge University Press
Print publication year: 2020
Creative Commons
Creative Common License - CCCreative Common License - BY
This content is Open Access and distributed under the terms of the Creative Commons Attribution licence CC-BY 4.0 https://creativecommons.org/cclicenses/

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