Bayesian Optimization: Difference between revisions
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== Introduction == | == 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. | 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. <ref>http://krasserm.github.io/2018/03/21/bayesian-optimization/</ref> | ||
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. | 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<ref>https://arxiv.org/abs/1012.2599</ref>, 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. <ref>https://dash.harvard.edu/bitstream/handle/1/27769882/BayesOptLoop.pdf;sequence=1</ref> | |||
== Theory, Methodology and or Algorithmic Discussion == | == Theory, Methodology and or Algorithmic Discussion == | ||
Bayesian Optimization incorporates the prior belief about | Bayesian Optimization incorporates the prior belief about | ||
== References == | |||
1) http://krasserm.github.io/2018/03/21/bayesian-optimization/ | |||
2) https://arxiv.org/abs/1012.2599 | |||
3) https://dash.harvard.edu/bitstream/handle/1/27769882/BayesOptLoop.pdf;sequence=1 | |||
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Revision as of 19:08, 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
References
1) http://krasserm.github.io/2018/03/21/bayesian-optimization/
2) https://arxiv.org/abs/1012.2599
3) https://dash.harvard.edu/bitstream/handle/1/27769882/BayesOptLoop.pdf;sequence=1
4)