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Related Questions
- Can you explain the trade-offs between different acquisition functions in Bayesian optimization, such as expected improvement (EI), probability of improvement (PI), and knowledge gradient (KG)?
- How does the choice of acquisition function affect the exploration-exploitation trade-off in Bayesian optimization?
- What is the impact of acquisition function choice on the number of iterations required to achieve a certain level of accuracy in Bayesian optimization?
- Can you discuss the relationship between the acquisition function and the sample size in Bayesian optimization?
- How does the choice of acquisition function influence the convergence behavior of Bayesian optimization algorithms?
- What are the implications of choosing a specific acquisition function on the computational efficiency of Bayesian optimization in terms of time and resources?
- Can you compare the performance of different acquisition functions in terms of their ability to balance exploration and exploitation in Bayesian optimization?
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