probcomp/Gen.jl

Health v2 · maintenance only

Adoption not part of the score

1850 stars · 164 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

Gen.jl is a general-purpose probabilistic programming system embedded in Julia that lets users write generative models as probabilistic programs and perform Bayesian inference on them. It provides programmable inference algorithms including MCMC, sequential Monte Carlo, and variational inference, with APIs for building custom proposals, kernels, and variational families without extending the compiler.

Use cases

• write generative models as probabilistic programs in julia
• run bayesian inference with mcmc or sequential monte carlo
• train generative models with gradient-based parameter optimization
• build custom inference algorithms like mcmc kernels and smc updates
• combine neural networks with model-based probabilistic inference
• perform bayesian structure learning with involutive mcmc
• speed up inference on models with dynamic stochastic structure
• implement custom distributions and generative functions with well-defined apis

When to choose

• you need programmable inference beyond a fixed engine, with custom proposals, kernels, or variational families
• you want to write generative models in an expressive julia-based modeling language
• you need hybrid algorithms mixing neural networks, variational inference, SMC, and MCMC
• your models have dynamic or stochastic structure requiring incremental computation
• you work in julia and want automatic differentiation plus inference-specific operations

When to avoid

• you need a production ML stack in python or another ecosystem
• you only need standard supervised deep learning without probabilistic modeling
• you require a turnkey inference engine with no algorithm customization
• your team cannot work in julia or with research-grade tooling

Facets

library · maturity active

machine-learning deep-learning simulation math parser machine-learning artificial-intelligence deep-learning computer-vision robotics programming-languages jvm windows cli probabilistic-programming bayesian-inference julia generative-models monte-carlo variational-inference mcmc differentiable-programming statistical-modeling inference-algorithms algorithms linux macos

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markdown · JSON · MCP: product_card(name="probcomp/Gen.jl")