Inverse problems are ubiquitous in physics. In high-energy physics inverse problems appear in the determination of Parton Distribution Functions, and in the extraction of spectral densities from lattice data. In this talk I will discuss a Bayesian approach to inverse problems and compare the results with other approaches like Backus-Gilbert and neural networks.