research
∙
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...
share
research
∙
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...
research
∙
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...
research
∙
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...
research
∙
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...
research
∙
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...
research
∙
07/16/2019
Improving Bayesian Local Spatial Models in Large Data Sets
Environmental processes resolved at a sufficiently small scale in space ...
research
∙
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...
research
∙
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...
research
∙
11/10/2017