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pith:Y2TLRLEL

pith:2018:Y2TLRLELJAS7P6FWPCQWHASXR7

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A Tutorial on Bayesian Optimization

Peter I. Frazier

Bayesian optimization builds a Gaussian process surrogate for an expensive objective and uses an acquisition function to choose each next evaluation point.

arxiv:1807.02811 v1 · 2018-07-08 · stat.ML · cs.LG · math.OC

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Claims

C1strongest claim

We provide a generalization of expected improvement to noisy evaluations, justified by a formal decision-theoretic argument, standing in contrast to previous ad hoc modifications.

C2weakest assumption

The tutorial assumes that a Gaussian process provides an adequate surrogate model for the objective function and that the reader has sufficient background in Bayesian methods and Gaussian processes.

C3one line summary

Bayesian optimization uses Gaussian process regression to build a surrogate model and acquisition functions to guide sampling for optimizing costly objective functions, including a new formal generalization of expected improvement to noisy evaluations.

References

101 extracted · 101 resolved · 4 Pith anchors

[1] O., Shahriari, B., and Schmidt, M 2016

[2] Berger, J. O. (2013). Statistical Decision Theory and Bayesian Analysis . Springer Science & Business Media 2013

[3] Blum, J. R. (1954). Multidimensional stochastic approximation methods. The Annals of Mathematical Statistics , pages 737--744 1954

[4] Booker, A., Dennis, J., Frank, P., Serafini, D., Torczon, V., and Trosset, M. (1999). A rigorous framework for optimization of expensive functions by surrogates . Structural and Multidisciplinary Opti 1999

[5] Bottou, L. (2012). Stochastic gradient descent tricks. In Montavon, G., Orr, G. B., and M \"u ller, K. R., editors, Neural Networks: Tricks of the Trade , pages 421--436. Springer 2012

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Cited by

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First computed	2026-07-04T22:53:53.807374Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

c6a6b8ac8b4825f7f8b678a16382578ff4fccc0b5ad990dd263275c7f2e8405b

Aliases

arxiv: 1807.02811 · arxiv_version: 1807.02811v1 · doi: 10.48550/arxiv.1807.02811 · pith_short_12: Y2TLRLELJAS7 · pith_short_16: Y2TLRLELJAS7P6FW · pith_short_8: Y2TLRLEL

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curl -sH 'Accept: application/ld+json' https://pith.science/pith/Y2TLRLELJAS7P6FWPCQWHASXR7 \
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# expect: c6a6b8ac8b4825f7f8b678a16382578ff4fccc0b5ad990dd263275c7f2e8405b

Canonical record JSON

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