19 citations · 25 across the 12 of their papers we have counts for
6 papers · 1 filter
High-resolution Bayesian mapping of landslide hazard with unobserved trigger event
Thomas Opitz, Haakon Bakka, Raphaël Huser +1
Statistical models for landslide hazard enable mapping of risk factors and landslide occurrence intensity by using geomorphological covariates available at high spatial resolution.…
Modeling Non-Stationary Temperature Maxima Based on Extremal Dependence Changing with Event Magnitude
Peng Zhong, Raphaël Huser, Thomas Opitz
The modeling of spatio-temporal trends in temperature extremes can help better understand the structure and frequency of heatwaves in a changing climate. Here, we study annual temp…
Bayesian space-time gap filling for inference on extreme hot-spots: an application to Red Sea surface temperatures
Daniela Castro-Camilo, Linda Mhalla, Thomas Opitz
We develop a method for probabilistic prediction of extreme value hot-spots in a spatio-temporal framework, tailored to big datasets containing important gaps. In this setting, dir…
Landscape allocation: stochastic generators and statistical inference
Patrizia Zamberletti, Julien Papaïx, Edith Gabriel +1
In agricultural landscapes, the composition and spatial configuration of cultivated and semi-natural elements strongly impact species dynamics, their interactions and habitat conne…
Point-process based Bayesian modeling of space-time structures of forest fire occurrences in Mediterranean France
Thomas Opitz, Florent Bonneu, Edith Gabriel
Due to climate change and human activity, wildfires are expected to become more frequent and extreme worldwide, causing economic and ecological disasters. The deployment of prevent…
Semi-parametric resampling with extremes
Thomas Opitz, Denis Allard, Grégoire Mariéthoz
Nonparametric resampling methods such as Direct Sampling are powerful tools to simulate new datasets preserving important data features such as spatial patterns from observed datas…