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Related Questions
- Can Simple Random Sampling (SRS) effectively explore the entire search space in Bayesian optimization?
- Does SRS require a large number of samples to accurately estimate the posterior distribution of the objective function?
- Can SRS handle high-dimensional search spaces and complex objective functions?
- How does SRS perform compared to other surrogate models, such as Gaussian processes or neural networks?
- Are there any scenarios where SRS is a good choice as a surrogate model in Bayesian optimization?
- Can SRS be used to handle expensive function evaluations in Bayesian optimization?
- How does the choice of sample size affect the performance of SRS as a surrogate model in Bayesian optimization?
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