Design and Implementation of Probabilistic Programming Language Anglican

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

doi:10.1145/3064899.3064910

Cited by 72 publications

(79 citation statements)

References 16 publications

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“…Probabilistic programs are shown to be powerful models for a wide variety of applications, such as analysis of stochastic network protocols [40,65,88], machine learning applications [26,44,81,83], and robot planning [91,92], to name a few. There are also many probabilistic programming languages (such as Church [42], Anglican [93] and WebPPL [43]) and automated analysis of such programs is an active research area in formal methods and programming languages (see [1,16,18,22,37,66,67,74,96]). Nondeterministic programs.…”

Section: Introductionmentioning

confidence: 99%

Cost analysis of nondeterministic probabilistic programs

Wang

Goharshady

et al. 2019

Proceedings of the 40th ACM SIGPLAN Conference on Programming Language Design and Implementation

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We consider the problem of expected cost analysis over nondeterministic probabilistic programs, which aims at automated methods for analyzing the resource-usage of such programs. Previous approaches for this problem could only handle nonnegative bounded costs. However, in many scenarios, such as queuing networks or analysis of cryptocurrency protocols, both positive and negative costs are necessary and the costs are unbounded as well.In this work, we present a sound and efficient approach to obtain polynomial bounds on the expected accumulated cost of nondeterministic probabilistic programs. Our approach can handle (a) general positive and negative costs with bounded updates in variables; and (b) nonnegative costs with general updates to variables. We show that several natural examples which could not be handled by previous approaches are captured in our framework.Moreover, our approach leads to an efficient polynomialtime algorithm, while no previous approach for cost analysis of probabilistic programs could guarantee polynomial runtime. Finally, we show the effectiveness of our approach using experimental results on a variety of programs for which we efficiently synthesize tight resource-usage bounds.

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Section: Introductionmentioning

confidence: 99%

Cost analysis of nondeterministic probabilistic programs

Wang

Goharshady

et al. 2019

Proceedings of the 40th ACM SIGPLAN Conference on Programming Language Design and Implementation

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“…Such programs provide a flexible framework for many different applications, ranging from the analysis of network protocols [Foster et al 2016;Kahn 2017;Smolka et al 2017], to machine learning applications Ścibior et al 2015], and robot planning [Thrun 2000[Thrun , 2002. The recent interest in probabilistic programs has led to many probabilistic programming languages (such as Church , Anglican [Tolpin et al 2016] and WebPPL [Goodman and Stuhlmüller 2014]) and their analysis is an active research area in formal methods and programming languages (see [Agrawal et al 2018;Chakarov and Sankaranarayanan 2013;Chatterjee et al 2016a,b;Esparza et al 2012;Ngo et al 2018;Wang et al 2018]).…”

Section: Introductionmentioning

confidence: 99%

Modular verification for almost-sure termination of probabilistic programs

Huang

Chatterjee

et al. 2019

Proc. ACM Program. Lang.

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In this work, we consider the almost-sure termination problem for probabilistic programs that asks whether a given probabilistic program terminates with probability 1. Scalable approaches for program analysis often rely on modularity as their theoretical basis. In non-probabilistic programs, the classical variant rule (V-rule) of Floyd-Hoare logic provides the foundation for modular analysis. Extension of this rule to almost-sure termination of probabilistic programs is quite tricky, and a probabilistic variant was proposed in [Fioriti and Hermanns 2015]. While the proposed probabilistic variant cautiously addresses the key issue of integrability, we show that the proposed modular rule is still not sound for almost-sure termination of probabilistic programs.Besides establishing unsoundness of the previous rule, our contributions are as follows: First, we present a sound modular rule for almost-sure termination of probabilistic programs. Our approach is based on a novel notion of descent supermartingales. Second, for algorithmic approaches, we consider descent supermartingales that are linear and show that they can be synthesized in polynomial time. Finally, we present experimental results on a variety of benchmarks and several natural examples that model various types of nested while loops in probabilistic programs and demonstrate that our approach is able to efficiently prove their almost-sure termination property.

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“…However to achieve this, the host language must coincide with the implementation language of the probabilistic model, on one hand, and allow a computationally efficient implementation of the simulation, on the other hand. Some probabilistic programming frameworks (Figaro [36], Anglican [51], Turing [11]) are built with tight integration with the host environment in mind; more often than not though the probabilistic code is not trivial to re-use.…”

Section: Challengesmentioning

confidence: 99%

“…To use a distribution in a differentiable model, the distribution's log probability density function must be differentiable by both the distribution's parameters and by the value of the random variable. Distributions are usually supplied as a part of the probabilistic programming framework [10,11,51], and, if at all possible, adding a user-defined distribution requires programming at a lower level of abstraction than while specifying a probabilistic model [1,41,51].…”

Section: Model Compositionmentioning

confidence: 99%

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Deployable probabilistic programming

Tolpin¹

2019

Proceedings of the 2019 ACM SIGPLAN International Symposium on New Ideas, New Paradigms, and Reflections on Programming and Sof

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We propose design guidelines for a probabilistic programming facility suitable for deployment as a part of a production software system. As a reference implementation, we introduce Infergo, a probabilistic programming facility for Go, a modern programming language of choice for server-side software development. We argue that a similar probabilistic programming facility can be added to most modern generalpurpose programming languages.Probabilistic programming enables automatic tuning of program parameters and algorithmic decision making through probabilistic inference based on the data. To facilitate addition of probabilistic programming capabilities to other programming languages, we share implementation choices and techniques employed in development of Infergo. We illustrate applicability of Infergo to various use cases on case studies, and evaluate Infergo's performance on several benchmarks, comparing Infergo to dedicated inference-centric probabilistic programming frameworks.

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Design and Implementation of Probabilistic Programming Language Anglican

Cited by 72 publications

References 16 publications

Cost analysis of nondeterministic probabilistic programs

Cost analysis of nondeterministic probabilistic programs

Modular verification for almost-sure termination of probabilistic programs

Deployable probabilistic programming

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