  In machine learning, likelihood-free inference refers to the task of
performing an analysis driven by data instead of an analytical expression. We
discuss the application of Neural Spline Flows, a neural density estimation
algorithm, to the likelihood-free inference problem of the measurement of
neutrino oscillation parameters in Long Baseline neutrino experiments. A method
adapted to physics parameter inference is developed and applied to the case of
the disappearance muon neutrino analysis at the T2K experiment.
