Bayesian optimization of function networks involves using Bayesian methods to optimize the hyperparameters of a network of functions. It utilizes a surrogate model, often a Gaussian process, to represent the objective function and an acquisition function to guide the search for the optimal hyperparameters. The process involves iteratively acquiring data, fitting the surrogate model to the data, and updating the acquisition function to select the next hyperparameter values to explore. By considering uncertainty in the objective function and efficiently exploring the parameter space, Bayesian optimization can effectively tune hyperparameters for complex function networks.
