Will Welch's Suggested Papers (Updated 2024/03/20)
P. I. Frazier, "A Tutorial on Bayesian Optimization", 2018, https://arxiv.org/abs/1807.02811 (TAKEN)
The paper provides a review of Bayesian Optimization, a method for finding the global optimum of a blackbox function.
Tasks:

Give a summary of the overall strategy employed by Bayesian optimization.

In particular give an intuitive description of the various acquisition functions to guide the algorithm.

There are many wellknown test functions, such as GoldsteinPrice and Hartmann 6. Choose say 2 or 3 to compare methods in an experiment you will design, analyse, and write up.
 There are two aspects (factors) of interest to me when comparing "methods".
The first factor is the implementation: compare a python implementation such as BoTorch with the R library DiceOptim. The second factor is the acquisition function: try several as time allows,
taking account of what is available in the two implementations.
 You will have to choose a metric to assess the effectiveness of a method on a particular test problem.
 This is a statistical experiment. It is important to use the fundamental principles of statistical design and analysis.
 Feel free to limit any aspect that is causing you difficulty, with an explanation.
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