# Design and Implementation of Probabilistic Programming Language Anglican

arXiv: https://arxiv.org/abs/1608.05263 · HTML: https://mlsanity.com/paper/1608.05263/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Aug 2016.

## Built on by (most cited first, 15 of 15)

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- [Rethinking Variational Inference for Probabilistic Programs with Stochastic Support](https://mlsanity.com/paper/2311.00594/) (2023)
- [Meta-Learning an Inference Algorithm for Probabilistic Programs](https://mlsanity.com/paper/2103.00737/) (2021)
- [Control-Data Separation and Logical Condition Propagation for Efficient Inference on Probabilistic Programs](https://mlsanity.com/paper/2101.01502/) (2021)
- [On Reinforcement Learning, Effect Handlers, and the State Monad](https://mlsanity.com/paper/2203.15426/) (2022)
- [Borch: A Deep Universal Probabilistic Programming Language](https://mlsanity.com/paper/2209.06168/) (2022)
- [Beyond Bayesian Model Averaging over Paths in Probabilistic Programs with Stochastic Support](https://mlsanity.com/paper/2310.14888/) (2023)
- [Automatic Rao-Blackwellization for Sequential Monte Carlo with Belief Propagation](https://mlsanity.com/paper/2312.09860/) (2023)
- [Inference Plans for Hybrid Particle Filtering](https://mlsanity.com/paper/2408.11283/) (2024)

## 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.