Information field based global Bayesian inference of the jet transport coefficient

Understand

Bayesian statistical inference is a powerful tool for model-data comparisons and extractions of physical parameters that are often unknown functions of system variables.

• Existing Bayesian analyses often rely on explicit parametrizations of the unknown function.
• It can introduce long-range correlations that impose fictitious constraints on physical parameters in regions of the variable space that are not probed by the experimental data.
• We develop an information field (IF) approach to modeling the prior distribution of the unknown function that is free of long-range correlations.