Bayesian Optimization

Author : By Deepa Korani (dmk333@cornell.edu)

Steward : Fenqgi You

Introduction

Bayesian Optimization is a sequential model-based approach to solving problems. In particular, it prescribes a prior belief over the possible objective functions, and then sequentially refine the model as data are observed via Bayesian posterior updating. {https://dash.harvard.edu/bitstream/handle/1/27769882/BayesOptLoop.pdf;sequence=1}

Bayesian Optimization is useful in machine learning. Since Machine Learning consists of black box optimization problem where the objective function is a black box function, where the analytical expression for the function is unknown, Bayesian optimization can be useful here. They attempt to find the global optimum in a minimum number of steps. {http://krasserm.github.io/2018/03/21/bayesian-optimization/}

Theory, Methodology and or Algorithmic Discussion

Bayesian Optimization incorporates the prior belief about