A Live, Multiple-Representation Probabilistic Programming Environment for Novices

Proc. ACM Program. Lang.

Sutton

2019

Self Cite

Stan is a probabilistic programming language that has been increasingly used for real-world scalable projects. However, to make practical inference possible, the language sacrifices some of its usability by adopting a block syntax, which lacks compositionality and flexible user-defined functions. Moreover, the semantics of the language has been mainly given in terms of intuition about implementation, and has not been formalised.This paper provides a formal treatment of the Stan language, and introduces the probabilistic programming language SlicStan -a compositional, self-optimising version of Stan. Our main contributions are (1) the formalisation of a core subset of Stan through an operational density-based semantics; (2) the design and semantics of the Stan-like language SlicStan, which facilities better code reuse and abstraction through its compositional syntax, more flexible functions, and information-flow type system; and (3) a formal, semanticpreserving procedure for translating SlicStan to Stan.

Section: Usability Of Probabilistic Programming Languagesmentioning

confidence: 99%

Probabilistic programming with densities in SlicStan: efficient, flexible, and deterministic

Gorinova

Proc. ACM Program. Lang.

Sutton

2019

Self Cite

“…More recently, Stan [6] and PyMC3 [21] have also gained wide popularity, and there is a wide range of research languages, including Figaro [27], Anglican [37], and many others. Probabilistic programming environments with graphical representations have also been developed, to aid the understanding of programmers new to the paradigm [13]. Table 1: Estimated users of probabilistic programming and of spreadsheets.…”

Section: Background: Probabilistic Programmingmentioning

confidence: 99%

End-User Probabilistic Programming

Borghouts

Quantitative Evaluation of Systems

Sarkar

et al. 2019

Self Cite

Probabilistic programming aims to help users make decisions under uncertainty. The user writes code representing a probabilistic model, and receives outcomes as distributions or summary statistics. We consider probabilistic programming for end-users, in particular spreadsheet users, estimated to number in tens to hundreds of millions. We examine the sources of uncertainty actually encountered by spreadsheet users, and their coping mechanisms, via an interview study. We examine spreadsheet-based interfaces and technology to help reason under uncertainty, via probabilistic and other means. We show how uncertain values can propagate uncertainty through spreadsheets, and how sheet-defined functions can be applied to handle uncertainty. Hence, we draw conclusions about the promise and limitations of probabilistic programming for end-users.

“…We propose that the grid, and its formula syntax, be left untouched, but to provide opportunities for abstraction through additional representations. We build on the theory of multiple representations that originates in Ainsworth's research in mathematics education [4] but has found widespread applications in computer science education [5], [6], and enduser programming research [7]. By offering multiple representations of the same core object (in our case, the program exemplified by the spreadsheet), we can help the user learn to move fluently between different levels of abstraction, choosing the abstraction appropriate for the task at hand.…”

Section: A Problem: Errors In Spreadsheetsmentioning

confidence: 99%

“…Excel's 'calculated columns' 6 apply a single formula to an entire column, but using a more abstract 'structured reference' syntax, and there is no way to create a calculated 'row' or 'block'. Excel's array formulas 7 use an abstract syntax to assign a single formula to a block of cells, but violate Kay's 'value principle' [35] by forbidding inspection or editing of any of the constituent cells except the header. Solutions of this second approach address the poor abstraction gradient in spreadsheets [36], but require substantially greater expertise to use.…”

Section: B Overcoming Spreadsheet Errorsmentioning

confidence: 99%

See 1 more Smart Citation

Calculation View: multiple-representation editing in spreadsheets

Sarkar

2018 IEEE Symposium on Visual Languages and Human-Centric Computing (VL/HCC)

Jones

et al. 2018

Self Cite

Spreadsheet errors are ubiquitous and costly, an unfortunate combination that is well-reported. A large class of these errors can be attributed to the inability to clearly see the underlying computational structure, as well as poor support for abstraction (encapsulation, re-use, etc). In this paper we propose a novel solution: a multiple-representation spreadsheet containing additional representations that allow abstract operations, without altering the conventional grid representation or its formula syntax. Through a user study, we demonstrate that the use of multiple representations can significantly improve user performance when performing spreadsheet authoring and debugging tasks. We close with a discussion of design implications and outline future directions for this line of inquiry.