Speaker
Description
Despite its ability to quantify uncertainties probabilistically and naturally accommodate theoretical constraints, Bayesian inference has so far played a limited role in PDF fitting. In this talk I will present Colibri, an open-source code that provides a general and flexible tool for PDF fits. The code is built so that users can implement their own PDF model, and use built-in functionalities for a fast computation of observables. It grants easy access to experimental data, several error propagation methodologies, including the Hessian method, the Monte Carlo replica method, and an efficient numerical Bayesian sampling algorithm. To demonstrate the capabilities of Colibri, I will present a simple application: a polynomial PDF parametrisation. I further discuss how the functionalities illustrated in this example can be extended to more complex PDF parametrisations. In particular, Bayesian sampling in Colibri provides a framework for systematic model selection and model averaging, making it a valuable tool for benchmarking and combining different PDF parametrisations on solid statistical grounds.
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