BlackJAX: Composable Bayesian inference in JAX
2024
Online
report
BlackJAX is a library implementing sampling and variational inference algorithms commonly used in Bayesian computation. It is designed for ease of use, speed, and modularity by taking a functional approach to the algorithms' implementation. BlackJAX is written in Python, using JAX to compile and run NumpPy-like samplers and variational methods on CPUs, GPUs, and TPUs. The library integrates well with probabilistic programming languages by working directly with the (un-normalized) target log density function. BlackJAX is intended as a collection of low-level, composable implementations of basic statistical 'atoms' that can be combined to perform well-defined Bayesian inference, but also provides high-level routines for ease of use. It is designed for users who need cutting-edge methods, researchers who want to create complex sampling methods, and people who want to learn how these work.
Comment: Companion paper for the library https://github.com/blackjax-devs/blackjax Update: minor changes and updated the list of authors to include technical contributors
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BlackJAX: Composable Bayesian inference in JAX
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Autor/in / Beteiligte Person: | Cabezas, Alberto ; Corenflos, Adrien ; Lao, Junpeng ; Louf, Rémi ; Carnec, Antoine ; Chaudhari, Kaustubh ; Cohn-Gordon, Reuben ; Coullon, Jeremie ; Deng, Wei ; Duffield, Sam ; Durán-Martín, Gerardo ; Elantkowski, Marcin ; Foreman-Mackey, Dan ; Gregori, Michele ; Iguaran, Carlos ; Kumar, Ravin ; Lysy, Martin ; Murphy, Kevin ; Orduz, Juan Camilo ; Patel, Karm ; Wang, Xi ; Zinkov, Rob |
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Veröffentlichung: | 2024 |
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