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Related Questions
- How does Bayesian optimization leverage Gaussian processes to model the objective function and inform the search process?
- Can you elaborate on the role of the acquisition function in Bayesian optimization, particularly in determining the balance between exploration and exploitation?
- How does Bayesian optimization adapt to non-linear relationships between inputs and outputs, and what techniques are used to handle such complexities?
- What are the key differences between Bayesian optimization and other sequential optimization methods, such as grid search and random search?
- Can you provide an example of a real-world application where Bayesian optimization has been successfully used to optimize a complex objective function?
- How does Bayesian optimization handle multimodal objectives, where the optimal solution may have multiple local optima?
- What are some common pitfalls or challenges in implementing Bayesian optimization, and how can they be addressed?
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