Verifying the Effect of Initial Sample Size in Bayesian Optimization

Obtaining the level of a factor that has the greatest effect on product development or crop cultivation is important given limited time, money, labor, and resources. Design of experiments is an approach to efficiently collect data in order to clarify the relationship between factors and effect. However, design of experiments mainly ends with the initial experimental design, which makes it difficult to conduct several more experiments based on the results. On the other hand, Bayesian Optimization, one of the methods of inverse estimation, uses Latin Hypercube design as the initial design of experiments and then conduct experiments sequentially. This is a powerful advantage of Bayesian Optimization.