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08/08/2023
Calibrated Forecasts of Quasi-Periodic Climate Processes with Deep Echo State Networks and Penalized Quantile Regression
Among the most relevant processes in the Earth system for human habitabi...
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02/06/2023
High Resolution Global Precipitation Downscaling with Latent Gaussian Models and Nonstationary SPDE Structure
Obtaining high-resolution maps of precipitation data can provide key ins...
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12/24/2021
Sensitivity Analysis of Wind Energy Resources with Bayesian non-Gaussian and nonstationary Functional ANOVA
The transition from non-renewable to renewable energies represents a glo...
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05/06/2021
Calibration of Spatial Forecasts from Citizen Science Urban Air Pollution Data with Sparse Recurrent Neural Networks
With their continued increase in coverage and quality, data collected fr...
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02/01/2021
Forecasting High-Frequency Spatio-Temporal Wind Power with Dimensionally Reduced Echo State Networks
Fast and accurate hourly forecasts of wind speed and power are crucial i...
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11/02/2020
Assessing the Reliability of Wind Power Operations under a Changing Climate with a Non-Gaussian Distributional Adjustment
Facing increasing societal and economic pressure, many countries have es...
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07/16/2019
Improving Bayesian Local Spatial Models in Large Data Sets
Environmental processes resolved at a sufficiently small scale in space ...
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06/29/2018
Marginally Parametrized Spatio-Temporal Models and Stepwise Maximum Likelihood Estimation
In order to learn the complex features of large spatio-temporal data, mo...
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12/06/2017
A Multi-Resolution Spatial Model for Large Datasets Based on the Skew-t Distribution
Large, non-Gaussian spatial datasets pose a considerable modeling challe...
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11/10/2017
