Design and Implementation of Probabilistic Programming Language Anglican
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Abstract (arXiv)
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 macro-based 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.
Key facts. Cited by 15 papers in the map (data through 7 Oct 2026). First posted on arXiv 18 Aug 2016.
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Citing papers per year: 2021: 3; 2022: 5; 2023: 6; 2024: 1.
15 papers.
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- 2022Language Model Cascadescited by 104
- 2023Neural Probabilistic Logic Programming in Discrete-Continuous Domainscited by 24
- 2021Learning Proposals for Probabilistic Programs with Inference Combinatorscited by 6
- 2022Foundation Posteriors for Approximate Probabilistic Inferencecited by 6
- 2022Smoothness Analysis for Probabilistic Programs with Application to Optimised Variational Inferencecited by 5
- 2023Approximate Computing Survey, Part II: Application-Specific & Architectural Approximation Techniques and Applicationscited by 3
- 2023A Heavy-Tailed Algebra for Probabilistic Programmingcited by 2
- 2023Rethinking Variational Inference for Probabilistic Programs with Stochastic Supportcited by 2
- 2021Meta-Learning an Inference Algorithm for Probabilistic Programscited by 1
- 2021Control-Data Separation and Logical Condition Propagation for Efficient Inference on Probabilistic Programscited by 0
- 2022On Reinforcement Learning, Effect Handlers, and the State Monadcited by 0
- 2022Borch: A Deep Universal Probabilistic Programming Languagecited by 0
- 2024Inference Plans for Hybrid Particle Filtering21 Aug 2024
- 2023Automatic Rao-Blackwellization for Sequential Monte Carlo with Belief Propagation15 Dec 2023
- 2023Rethinking Variational Inference for Probabilistic Programs with Stochastic Support1 Nov 2023
- 2023Beyond Bayesian Model Averaging over Paths in Probabilistic Programs with Stochastic Support23 Oct 2023
- 2023Approximate Computing Survey, Part II: Application-Specific & Architectural Approximation Techniques and Applications20 Jul 2023
- 2023A Heavy-Tailed Algebra for Probabilistic Programming15 Jun 2023
- 2023Neural Probabilistic Logic Programming in Discrete-Continuous Domains8 Mar 2023
- 2022Borch: A Deep Universal Probabilistic Programming Language13 Sep 2022
- 2022Smoothness Analysis for Probabilistic Programs with Application to Optimised Variational Inference22 Aug 2022
- 2022Language Model Cascades21 Jul 2022
- 2022Foundation Posteriors for Approximate Probabilistic Inference19 May 2022
- 2022On Reinforcement Learning, Effect Handlers, and the State Monad29 Mar 2022
- 2022Language Model Cascadescited by 104
- 2023Neural Probabilistic Logic Programming in Discrete-Continuous Domainscited by 24
- 2021Learning Proposals for Probabilistic Programs with Inference Combinatorscited by 6
- 2022Foundation Posteriors for Approximate Probabilistic Inferencecited by 6
- 2022Smoothness Analysis for Probabilistic Programs with Application to Optimised Variational Inferencecited by 5
- 2023Approximate Computing Survey, Part II: Application-Specific & Architectural Approximation Techniques and Applicationscited by 3
- 2023A Heavy-Tailed Algebra for Probabilistic Programmingcited by 2
- 2023Rethinking Variational Inference for Probabilistic Programs with Stochastic Supportcited by 2
- 2021Meta-Learning an Inference Algorithm for Probabilistic Programscited by 1
- 2021Control-Data Separation and Logical Condition Propagation for Efficient Inference on Probabilistic Programscited by 0
- 2022On Reinforcement Learning, Effect Handlers, and the State Monadcited by 0
- 2022Borch: A Deep Universal Probabilistic Programming Languagecited by 0
12 most cited of 15 All 15 (JSON)
- 2024Inference Plans for Hybrid Particle Filtering21 Aug 2024
- 2023Automatic Rao-Blackwellization for Sequential Monte Carlo with Belief Propagation15 Dec 2023
- 2023Rethinking Variational Inference for Probabilistic Programs with Stochastic Support1 Nov 2023
- 2023Beyond Bayesian Model Averaging over Paths in Probabilistic Programs with Stochastic Support23 Oct 2023
- 2023Approximate Computing Survey, Part II: Application-Specific & Architectural Approximation Techniques and Applications20 Jul 2023
- 2023A Heavy-Tailed Algebra for Probabilistic Programming15 Jun 2023
- 2023Neural Probabilistic Logic Programming in Discrete-Continuous Domains8 Mar 2023
- 2022Borch: A Deep Universal Probabilistic Programming Language13 Sep 2022
- 2022Smoothness Analysis for Probabilistic Programs with Application to Optimised Variational Inference22 Aug 2022
- 2022Language Model Cascades21 Jul 2022
- 2022Foundation Posteriors for Approximate Probabilistic Inference19 May 2022
- 2022On Reinforcement Learning, Effect Handlers, and the State Monad29 Mar 2022
12 newest of 15 All 15 (JSON)