Explore a high energy physics seminar presentation that delves into the application of Neural Simulation-Based Inference (NSBI) for Higgs boson physics studies at the Large Hadron Collider (LHC) and ATLAS experiment. Learn about this advanced machine learning approach that enables high-dimensional parameter estimation without requiring data binning into simplified histograms. Discover how NSBI frameworks using neural networks for probability density ratio estimation can be implemented in full-scale LHC analysis, incorporating systematic uncertainties and confidence interval construction. Examine the practical application of this method through an off-shell Higgs boson couplings measurement case study in four leptons final states, demonstrating how this extension of standard LHC statistical frameworks can enhance various physics analyses.