Probabilistic Programming in Anglican

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Applications of Probabilistic Programming (Master's thesis, 2015) (2016, methodology): “…(Perov and Mansinghka, 2012)), by an intermediate compilation instead of a continuous interpretation (e.g. the recent Anglican implementation (Tolpin et al., 2015b), with a program compilation into a Clojure function, works faster than the previous Anglican interpreter (Wood et al., 2014)),…”
Probabilistic programming with programmable inference (2018, methodology): “Prominent examples of language/inference strategy pairs include Stan [3] with Hamiltonian Monte Carlo inference [2]; WebPPL [17] with Single-Variable Metropolis-Hastings inference, Black Box variational inference, and Hamiltonian Monte Carlo inference; and Anglican [47] with particle Gibbs sampling.”
MultiVerse: Causal Reasoning using Importance Sampling in Probabilistic Programming (2019, background): “Also, similar ideas can be implemented in existing probabilistic programming languages like Pyro, Anglican (Wood et al., 2014; Tolpin et al., 2015, 2016), Venture, Church (Goodman et al., 2012) language engines, Gen (Cusumano-Towner and Mansinghka, 2018), and others (Probabilistic Programming Wiki…”
A lambda-calculus foundation for universal probabilistic programming (2015, methodology): “C HURCH , and its descendants V ENTURE (Mansinghka et al. 2014), A NGLICAN (Tolpin et al. 2015), and W EB C HURCH (Goodman and Tenenbaum 2014) are dialects of S CHEME .”