Bayesian Optimization: Difference between revisions
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== References == | == References == | ||
Revision as of 19:09, 27 November 2021
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. [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[2], 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.
Bayesian Optimization has shown tremendous solutions for a wide variety of design problems. Certain application include; robotics, envrionmental monitoring, combinatorial optimization, adaptive Monte Carlo, reinforcement learning. [3]
Theory, Methodology and or Algorithmic Discussion
Bayesian Optimization incorporates the prior belief about