Probabilistic Programming in Anglican

Tolpin, David; Meent, Jan-Willem van de; Wood, Frank

doi:10.1007/978-3-319-23461-8_36

Cited by 30 publications

(38 citation statements)

References 1 publication

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Church [10] introduced the idea of a universal probabilistic programming language, that can express any model written in a Turing-complete programming language (although efficient inference in general remains a challenge). 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].…”

Section: Background: Probabilistic Programmingmentioning

confidence: 99%

End-User Probabilistic Programming

Borghouts

Gordon

Sarkar

et al. 2019

Quantitative Evaluation of Systems

View full text Add to dashboard 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.

show abstract

Section: Background: Probabilistic Programmingmentioning

confidence: 99%

End-User Probabilistic Programming

Borghouts

Gordon

Sarkar

et al. 2019

Quantitative Evaluation of Systems

View full text Add to dashboard Cite

show abstract

“…CHURCH, and its descendants VENTURE [24], ANGLICAN [37], and WEB CHURCH [13] are dialects of SCHEME. Another example of universal probabilistic programming is WEBPPL [12], a probabilistic interpretation of JAVASCRIPT.…”

Section: Universal Probabilistic Programming In Churchmentioning

confidence: 99%

“…Other probabilistic language implementations also use trace MCMC inference, including CHURCH [14], VENTURE [24], WEBPPL [12], and ANGLICAN [37]. These works focusing on efficiency and convergence properties, and do not state formal correctness claims for their implementations.…”

Section: Related Workmentioning

confidence: 99%

A lambda-calculus foundation for universal probabilistic programming

et al. 2016

View full text Add to dashboard Cite

We develop the operational semantics of an untyped probabilistic λ-calculus with continuous distributions, and both hard and soft constraints, as a foundation for universal probabilistic programming languages such as CHURCH, ANGLICAN, and VEN-TURE. Our first contribution is to adapt the classic operational semantics of λ-calculus to a continuous setting via creating a measure space on terms and defining step-indexed approximations. We prove equivalence of big-step and small-step formulations of this distribution-based semantics. To move closer to inference techniques, we also define the sampling-based semantics of a term as a function from a trace of random samples to a value. We show that the distribution induced by integration over the space of traces equals the distribution-based semantics. Our second contribution is to formalize the implementation technique of trace Markov chain Monte Carlo (MCMC) for our calculus and to show its correctness. A key step is defining sufficient conditions for the distribution induced by trace MCMC to converge to the distribution-based semantics. To the best of our knowledge, this is the first rigorous correctness proof for trace MCMC for a higher-order functional language, or for a language with soft constraints.

show abstract

“…While probabilistic programming systems shorten the iteration cycle in exploratory model design, they typically lack basic functionality needed for data I/O, pre-processing, and analysis and visualization of inference results. In this paper, we describe the implementation of Anglican (Tolpin et al 2015b;, a probabilistic programming language that tightly integrates with Clojure (Hickey 2008; Clo), a general-purpose programming language that runs on the Java Virtual Machine (JVM). Both languages share a common syntax, and can be invoked from each other.…”

Section: Introductionmentioning

confidence: 99%

Design and Implementation of Probabilistic Programming Language Anglican

Tolpin

Meent

Yang

et al. 2016

Proceedings of the 28th Symposium on the Implementation and Application of Functional Programming Languages

Self Cite

View full text Add to dashboard Cite

Anglican is a probabilistic programming system designed to interoperate with Clojure and other JVM languages. We introduce the programming language Anglican, outline our design choices, and discuss in depth the implementation of the Anglican language and runtime, including macrobased compilation, extended CPS-based evaluation model, and functional representations for probabilistic paradigms, such as a distribution, a random process, and an inference algorithm.We show that a probabilistic functional language can be implemented efficiently and integrated tightly with a conventional functional language with only moderate computational overhead. We also demonstrate how advanced probabilistic modeling concepts are mapped naturally to the functional foundation.

show abstract

Probabilistic Programming in Anglican

Cited by 30 publications

References 1 publication

End-User Probabilistic Programming

End-User Probabilistic Programming

A lambda-calculus foundation for universal probabilistic programming

Design and Implementation of Probabilistic Programming Language Anglican

Contact Info

Product

Resources

About