Boris Béranger
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    • ExtremalDep: Modelling extremal dependence in high-dimensional extremes
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    • Fast and flexible inference for spatial extremes
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    • Fitting max-stable processes to high-dimensional data
    • Using symbolic data to understand underlying data behaviour
    • Estimation and uncertainty quantification for extreme quantile regions
    • Fitting models to underlying data using aggregates
    • High-dimensional inference for max-stable processes
    • Estimation and uncertainty quantification for extreme quantile regions
    • High-dimensional inference for max-stable processes
    • Composite likelihood and logistic regression models for aggregated data
    • Estimating Equations for Data Summaries
    • Advances in the analysis of aggregated data
    • New Models for Symbolic Data
    • Inference for extremal-$t$ and skew-$t$ max-stable models in high dimensions
    • First steps in the analysis of Symbolic Data
    • A composite likelihood based approach for max-stable processes using histogram-valued variables
    • On some features of the skewed families of max-stable processes
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Pandas

Oct 26, 2023 · 1 min read
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Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures.

Last updated on Jul 24, 2025
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Boris Béranger
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Boris Béranger
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PyTorch Oct 26, 2023 →

© 2025 Boris Béranger. This work is licensed under CC BY NC ND 4.0

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