{"categories":[{"label":"Scientific/Engineering","url":"https://skillfed.io/packages/category/scientific-engineering/5"},{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence/7"},{"label":"Mathematics","url":"https://skillfed.io/packages/category/scientific-engineering-mathematics/2"},{"label":"Education","url":"https://skillfed.io/packages/category/education"}],"enrichment":{"capability":"BlackJAX provides a collection of MCMC and other sampling algorithms built on JAX that run on CPU and GPU, designed for users who have a log-probability function and need a performant sampler.","skillfed_tags":["mcmc","bayesian-inference","gpu-accelerated"],"use_cases":["Sample from a posterior distribution when you have a log-probability function but no full probabilistic programming framework.","Build custom MCMC samplers by composing elementary kernels (integrators, proposals, momentum generators) without reimplementing from scratch.","Run inference on GPU or TPU by leveraging JAX's hardware acceleration for large-scale Bayesian computations.","Integrate sampling into an existing probabilistic programming language by providing a modular, decoupled sampler.","Learn how sampling algorithms work by studying and experimenting with the library's composable, well-documented building blocks.","Accelerate research on new sampling schemes using robust, performant, and reusable algorithm components."],"what_it_does":"BlackJAX is a library of sampling algorithms\u2014primarily MCMC methods like NUTS\u2014built on top of JAX and designed to run efficiently on both CPU and GPU hardware. It is not a probabilistic programming language itself, but rather a collection of composable, reusable sampling kernels that integrate well with any tool that can provide a log-probability density function compatible with JAX.\n\nThe library targets two audiences: users who have a logpdf and just need a working sampler (via simple high-level APIs like `blackjax.nuts()`), and researchers building custom inference algorithms who want to reuse robust, well-tested building blocks like integrators, proposal generators, and momentum samplers. All kernels follow a stateless functional pattern\u2014taking a random key and state, returning a new state and metadata\u2014making them easy to compose and swap. The package depends on jax, jaxlib, numpy, optax, scipy, and typing-extensions.","worth_installing":"Yes. BlackJAX is actively maintained, has no known vulnerabilities, installs with low friction, and is licensed permissively. It is well-suited if you need MCMC sampling on JAX with GPU support, want composable algorithm building blocks, or are integrating sampling into a larger system. Not necessary if you only need sampling from standard distributions or are already using a full probabilistic programming framework."},"id":"blackjax","links":{"html":"https://skillfed.io/packages/blackjax","md":"https://skillfed.io/packages/blackjax.md","pypi":"https://pypi.org/project/blackjax/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-07-16","license_spdx":null,"license_treatment":"permissive","name":"blackjax","python_support":"supports_current","summary":"Flexible and fast sampling in Python"},"popularity":{"monthly_downloads":227060,"position":9189,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"1.6.2"}