This academic paper presents the parallel knowledge gradient method (q-KG), a novel approach for batch Bayesian optimization designed to efficiently find the global optimum of costly, derivative-free functions when multiple evaluations can be performed concurrently. Unlike previous methods that build batches greedily, q-KG uses a decision-theoretic analysis to select a set of points that is Bayes-optimal for sampling in a single iteration. The authors address the computational challenge of maximizing q-KG by developing an efficient gradient computation strategy based on infinitesimal perturbation analysis (IPA), demonstrating through experiments on synthetic and real-world machine learning problems that q-KG significantly outperforms existing parallel Bayesian optimization algorithms, particularly in the presence of noisy function evaluations.