In this talk, we explore the capabilities of JAX, a powerful library that builds upon NumPy’s foundations. JAX extends array operations and introduces essential tools for researchers, including automatic differentiation and just-in-time compilation. We will demonstrate how JAX can be harnessed to implement traditional algorithms used in Bayesian inference. With examples and hands-on exercises, we’ll show how JAX simplifies the coding process, enhances performance, and empowers researchers in the field of scientific computing and machine learning