Speaker
Description
In HEP, we often use Monte-Carlo simulation or bootstrapping to propagate errors in more complicated scenarios. However, standard error propagation could be done in most cases, if it was easy to compute the derivatives of the mapping function. Jacobi is a new library which offers a very powerful, fast, easy-to-use, and robust numerical derivative calculator. In contrast to libraries which do error propagation with with automatic differentiation, like the popular uncertainties library, Jacobi can compute derivatives for any analytical function, even if the function is opaque and calls into non-Python code. Jacobi is also completely non-intrusive, since it does not require one to replace the number and array types in the analysis with special number or array objects. In the talk, I show how to perform simple and more advanced error propagation with Jacobi.
