bayesian optimization

Bayesian optimization is a human-made sequential strategy for maximizing the value of an expensive black-box function when derivatives are unavailable. It constructs a probabilistic surrogate model — typically a Gaussian process — over the unknown objective, then iteratively selects evaluation points by optimizing an acquisition function (expected improvement, upper confidence bound, or probability of improvement) that balances exploration of uncertain regions with exploitation of known good values. The persistence mechanism is algorithmic: each iteration updates the surrogate posterior and re-optimizes the acquisition function until a stopping criterion (maximum iterations, budget exhaustion, or convergence) terminates the loop. [formal: optima bayesiana | substrate: behavior | horizon: hours | explicit: yes | epoch: 0.01]