probcomp/Gen.jl 排名第 9,364，此处有 8 个同类条目；anthropics/hh-rlhf 排名第 9,361。

jl A general-purpose probabilistic programming system with programmable inference, embedded in Julia. See for introduction, documentation, and tutorials. Features Multi-paradigm Bayesian inference via Sequential Monte Carlo, variational inference, MCMC, and more. Gradient-based training of generative models via parameter optimization, wake-sleep learning, etc. An expressive and intuitive modeling language for writing and composing probabilistic programs. Inference algorithms are _programmable_: Write custom proposals, variational families, MCMC kernels or SMC updates without worrying about the math. Support for Bayesian

structure learning via involutive MCMC and SMCP³. Specialized modeling constructs that speed-up inference by supporting incremental computation. Well-defined APIs for implementing custom generative models, distributions, gradients, etc. Installation The Gen package can be installed with the Julia package manager. From the Julia REPL, type to enter the Pkg REPL mode and then run: To install the latest development version, you may instead run: Gen can now be used in the Julia REPL, or at the top of a

script: To test the installation locally, you can run the tests with: Questions and Contributions If you have questions about using Gen. jl, feel free to open a discussion on GitHub. If you encounter a bug, please open an issue. We also welcome bug fixes and feature additions as pull requests. Please refer to our contribution guidelines for more details. jl is part of ongoing research at the MIT Probabilistic Computing Project. To get in contact, please email gen-contact

@mit. If you use Gen in your research, please cite our 2019 PLDI paper: *Gen: A General-Purpose Probabilistic Programming System with Programmable Inference. In Proceedings of the 40th ACM SIGPLAN Conference on Programming Language Design and Implementation (PLDI ‘19).