A tutorial on Gaussian process regression: modelling, exploring, and exploiting functions
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Liu et al. utilized a combination of covariance functions and mean functions in GPR for multi-step-ahead prognostics.
“To rectify this, Liu et al. [141] utilized a combination of covariance functions and mean functions in GPR for multi-step-ahead prognostics.”
From Data-driven health estimation and lifetime prediction of lithium-ion batteries: A review (2019) · cites this paper as [141]
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Data-driven health estimation and lifetime prediction of lithium-ion batteries: A review
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Online Health Diagnosis of Lithium-Ion Batteries Based on Nonlinear Autoregressive Neural Network
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- Authors
- Schulz, E., Speekenbrink, M., & Krause, A.
- Affiliations
- Harvard University (US)
- University College London (GB)
- ETH Zurich (CH)
- Corresponding author
- Schulz, Eric
- Venue
- Journal of Mathematical Psychology
- Publisher
- Elsevier BV
- Published
- 2018-08
- Volume
- 85
- Pages
- 1-16
- ISSN
- 0022-2496 (ISSN-L)
- OpenAlex
- W2804446681
- Cited by
- 1,735
- References
- 54
- Citations by year
- 2026: 254 · 2025: 449 · 2024: 340 · 2023: 273 · 2022: 172 · 2021: 121 · 2020: 96 · 2019: 21 · 2018: 7 · 2017: 1 · 2016: 1
- FWCI
- 34.48
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Journal of Mathematical Psychology (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Gaussian Processes and Bayesian Inference
- Social Sciences › Decision Sciences › Management Science and Operations Research › Advanced Bandit Algorithms Research
- Physical Sciences › Mathematics › Statistics and Probability › Probability and Statistical Research
- Keywords
- Gaussian process regression, exploration-exploitation, optimal experimental design, bandit problems, kernel functions, prior distributions, movie recommendation, psychological experiments
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