A model-learner pattern for bayesian reasoning

Gordon, Andrew D.; Aizatulin, Mihhail; Borgström, Johannes; Claret, Guillaume; Graepel, Thore; Nori, Aditya V.; Rajamani, Sriram K.; Russo, Claudio

doi:10.1145/2480359.2429119

Cited by 20 publications

(27 citation statements)

References 25 publications

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“…The original semantics of Tabular uses the higher-order model-learner pattern [12], based on a separate metalanguage. Given a Tabular schema S and an input database DB that matches S, our semantics consists of two algorithms.…”

Section: Spreadsheets and Typeful Probabilistic Programmingmentioning

confidence: 99%

Probabilistic Programs as Spreadsheet Queries

Gordon

Russo

Szymczak

et al. 2015

Programming Languages and Systems

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Abstract. We describe the design, semantics, and implementation of a probabilistic programming language where programs are spreadsheet queries. Given an input database consisting of tables held in a spreadsheet, a query constructs a probabilistic model conditioned by the spreadsheet data, and returns an output database determined by inference. This work extends probabilistic programming systems in three novel aspects: (1) embedding in spreadsheets, (2) dependently typed functions, and (3) typed distinction between random and query variables. It empowers users with knowledge of statistical modelling to do inference simply by editing textual annotations within their spreadsheets, with no other coding.

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Section: Spreadsheets and Typeful Probabilistic Programmingmentioning

confidence: 99%

Probabilistic Programs as Spreadsheet Queries

Gordon

Russo

Szymczak

et al. 2015

Programming Languages and Systems

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“…Our semantics is compositional: the model of a whole schema is assembled from models of tables, which themselves are composed from models of rows, assembled from models of individual cells. This formulation follows Gordon et al (2013a), with two refinements. First, when we apply a model to data, the model output is semi-observed, that is, each output is a pair consisting of an observed component (like a game outcome in TrueSkill) plus an unobserved latent component (like a performance in TrueSkill).…”

Section: Fun and The Model-learner Patternmentioning

confidence: 99%

“…Compared to the reference learner of Gordon et al (2013a), this new API can learn latent outputs since it works on semi-observed models. Our current implementation uses Infer.NET Fun to compute approximate marginal forms of the posterior distributions on the database parameter and latent database, and persists them to the relational store.…”

Section: A Reference Learner For Query-by-latent-columnmentioning

confidence: 99%

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Tabular

Gordon

Graepel

Rolland

et al. 2014

Proceedings of the 41st ACM SIGPLAN-SIGACT Symposium on Principles of Programming Languages

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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.

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“…Indeed, viewing algorithms as functions allows a smooth integration of distributions into the playground, itself nicely reflected at the level of types through monads [20,40]. As a matter of fact, many existing probabilistic programming languages [37,18] are designed around the λ-calculus or one of its incarnations, like Scheme.…”

Section: Introductionmentioning

confidence: 99%

On coinductive equivalences for higher-order probabilistic functional programs

Lago

Sangiorgi

Alberti

2014

Proceedings of the 41st ACM SIGPLAN-SIGACT Symposium on Principles of Programming Languages

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We study bisimulation and context equivalence in a probabilistic λ-calculus. The contributions of this paper are threefold. Firstly we show a technique for proving congruence of probabilistic applicative bisimilarity. While the technique follows Howe's method, some of the technicalities are quite different, relying on non-trivial "disentangling" properties for sets of real numbers. Secondly we show that, while bisimilarity is in general strictly finer than context equivalence, coincidence between the two relations is attained on pure λ-terms. The resulting equality is that induced by Levy-Longo trees, generally accepted as the finest extensional equivalence on pure λ-terms under a lazy regime. Finally, we derive a coinductive characterisation of context equivalence on the whole probabilistic language, via an extension in which terms akin to distributions may appear in redex position. Another motivation for the extension is that its operational semantics allows us to experiment with a different congruence technique, namely that of logical bisimilarity.

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A model-learner pattern for bayesian reasoning

Cited by 20 publications

References 25 publications

Probabilistic Programs as Spreadsheet Queries

Probabilistic Programs as Spreadsheet Queries

Tabular

On coinductive equivalences for higher-order probabilistic functional programs

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