Bayesian Optimization: Difference between revisions
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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<nowiki/>} | 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<nowiki/>} | ||
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, | 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/<nowiki/>} | ||
== 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 | ||
Revision as of 18:58, 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. {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