Turing: A Language for Flexible Probabilistic Inference

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The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

An Introduction to Probabilistic Programming (2018, neutral): “In the case of Turing (Ge et al., 2018), the implementing language (Julia) provides coroutines, which specify computations that may be interrupted and resumed later.”
MXFusion: A Modular Deep Probabilistic Programming Library (2018, background): “Probabilistic programming language (PPL, Lunn et al., 2000; Pfeffer, 2001; Goodman et al., 2008; Pfeffer, 2009; Murray, 2013; Paige & Wood, 2014; Minka et al., 2014; Carpenter et al., 2017; Ge et al., 2018) is a similar domain specific programming language that aims at describing probabilistic model”
Gen: a general-purpose probabilistic programming system with programmable inference (2019, methodology): “Systems besides Venture that provide some degree of inference programmability include WebPPL [23], Edward [61], Pyro [5], and Turing [18].”
Bean Machine: A Declarative Probabilistic Programming Language For Efficient Programmable Inference (2020, background): “The imperative Turing language (Ge et al., 2018) also implements compositional inference; however, it does not have the dynamic graph structure needed for efﬁcient MH updates.”
Pathfinder: Parallel quasi-Newton variational inference (2021, neutral): “jl (Ge et al., 2018), and other differentiable programming languages.”