Journal ArticleDOI
Bayesian Algorithms for One-Dimensional GlobalOptimization
TLDR
In this paper Bayesian analysis and Wiener process are used in orderto build an algorithm to solve the problem of globaloptimization and the Bayesian approach is exploited not only in the choice of the Wiener model but also in the estimation of the parameter σ2 of theWiener process.Abstract:
In this paper Bayesian analysis and Wiener process are used in order to build an algorithm to solve the problem of global optimization The paper is divided in two main parts In the first part an already known algorithm is considered: a new (Bayesian) stopping rule is added to it and some results are given, such as an upper bound for the number of iterations under the new stopping rule In the second part a new algorithm is introduced in which the Bayesian approach is exploited not only in the choice of the Wiener model but also in the estimation of the parameter \sigma^2 of the Wiener process, whose value appears to be quite crucial Some results about this algorithm are also givenread more
Citations
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Journal ArticleDOI
Efficient Global Optimization of Expensive Black-Box Functions
TL;DR: This paper introduces the reader to a response surface methodology that is especially good at modeling the nonlinear, multimodal functions that often occur in engineering and shows how these approximating functions can be used to construct an efficient global optimization algorithm with a credible stopping rule.
Journal ArticleDOI
Taking the Human Out of the Loop: A Review of Bayesian Optimization
TL;DR: This review paper introduces Bayesian optimization, highlights some of its methodological aspects, and showcases a wide range of applications.
Journal ArticleDOI
A Taxonomy of Global Optimization Methods Based on Response Surfaces
TL;DR: This paper presents a taxonomy of existing approaches for using response surfaces for global optimization, illustrating each method with a simple numerical example that brings out its advantages and disadvantages.
Posted Content
A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning
TL;DR: Bayesian optimization as mentioned in this paper employs the Bayesian technique of setting a prior over the objective function and combining it with evidence to get a posterior function, which permits a utility-based selection of the next observation to make on the objective functions, which must take into account both exploration (sampling from areas of high uncertainty) and exploitation, sampling areas likely to offer improvement over the current best observation.
Journal ArticleDOI
Information-Theoretic Regret Bounds for Gaussian Process Optimization in the Bandit Setting
TL;DR: This work analyzes an intuitive Gaussian process upper confidence bound algorithm, and bound its cumulative regret in terms of maximal in- formation gain, establishing a novel connection between GP optimization and experimental design and obtaining explicit sublinear regret bounds for many commonly used covariance functions.
References
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Jacob Wolfowitz,A. M. Mood +1 more
TL;DR: In this article, a tabular summary of parametric families of distributions is presented, along with a parametric point estimation method and a nonparametric interval estimation method for point estimation.