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Vecchia approximations lead in Gaussian process accuracy-runtime comparison

By PulseAugur Editorial · [1 sources] ·

Researchers have compared various scalable Gaussian process approximations for handling large spatial datasets. Their analysis focused on the trade-off between model accuracy and computational runtime across simulated and real-world data. The study found that Vecchia approximations consistently offered the best balance of accuracy and speed for likelihood evaluation, parameter estimation, and prediction. AI

IMPACT Provides a comparative analysis of computational methods for Gaussian processes, relevant for large-scale spatial data analysis in machine learning.

RANK_REASON Academic paper comparing computational methods for Gaussian processes. [lever_c_demoted from research: ic=1 ai=0.7]

Read on arXiv stat.ML →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Vecchia approximations lead in Gaussian process accuracy-runtime comparison

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Academic paper comparing computational methods for Gaussian processes. [lever_c_demoted from research: ic=1 ai=0.7]
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COVERAGE [1]

  1. arXiv stat.ML TIER_1 English(EN) · Filippo Rambelli, Fabio Sigrist ·

    An accuracy-runtime trade-off comparison of scalable Gaussian process approximations for spatial data

    arXiv:2501.11448v5 Announce Type: replace-cross Abstract: Gaussian processes (GPs) are flexible, probabilistic, nonparametric models widely used in fields such as spatial statistics and machine learning. A drawback of Gaussian processes is their computational cost, with $O(N^3)$ …

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