"Variational Calculus" by Florens, Mouchart, and Rolin (Springer Monographs in Mathematics) bridges classical functional analysis with modern Bayesian statistics, utilizing Hilbert spaces and variational operators for statistical modeling. It provides a foundational framework for variational inference in machine learning, exploring how to approximate complex probability distributions through functional derivatives. More information on this monograph can be found on the Springer website.
