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Explore the theoretical foundations and practical applications of Spatial Logistic Gaussian Processes in this seminar by Dr. Athénaïs Gautier from ONERA. Learn how to transform scattered data samples into comprehensive distribution fields through advanced spatial modeling techniques. Discover the mathematical framework underlying spatial logistic Gaussian processes and understand their role in uncertainty quantification and prediction. Examine real-world applications where these methods prove essential for handling spatial data with inherent uncertainty. Gain insights into the computational aspects of implementing these processes and their advantages over traditional spatial interpolation methods. Understand how these techniques bridge the gap between statistical theory and machine learning applications in spatial data analysis. This presentation is part of the "Representing, calibrating & leveraging prediction uncertainty from statistics to machine learning" event series at the Isaac Newton Institute.
