Neural networks for parameter estimation in intractable models
From MaRDI portal
Publication:6115548
DOI10.1016/j.csda.2023.107762arXiv2107.14346OpenAlexW3188640509MaRDI QIDQ6115548
Michael L. Stein, Julie Bessac, Amanda Lenzi, Johann Rudi
Publication date: 13 July 2023
Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/2107.14346
Cites Work
- Unnamed Item
- Unnamed Item
- Efficient inference and simulation for elliptical Pareto processes
- Likelihood estimators for multivariate extremes
- Approximate Bayesian computing for spatial extremes
- Bayesian inference for the Brown-Resnick process, with an application to extreme low temperatures
- On the robustness of maximum composite likelihood estimate
- Stationary max-stable fields associated to negative definite functions
- A spectral representation for max-stable processes
- Models for stationary max-stable random fields
- Scalable Bayesian inference for the inverse temperature of a hidden Potts model
- A two-step approach to model precipitation extremes in California based on max-stable and marginal point processes
- Bayesian Inference from Composite Likelihoods, with an Application to Spatial Extremes
- On the likelihood function of Gaussian max-stable processes
- Variograms for spatial max-stable random fields
- Adaptive approximate Bayesian computation
- Bivariate extreme value theory: Models and estimation
- Learning Summary Statistic for Approximate Bayesian Computation via Deep Neural Network
- Constructing Summary Statistics for Approximate Bayesian Computation: Semi-Automatic Approximate Bayesian Computation
- Statistical Inference for Max-Stable Processes in Space and Time
- The frontier of simulation-based inference
- An Introduction to Random Sets
- Likelihood-Based Inference for Max-Stable Processes
- Strictly Proper Scoring Rules, Prediction, and Estimation
- Exploiting occurrence times in likelihood inference for componentwise maxima
- The elements of statistical learning. Data mining, inference, and prediction
- Statistical modeling of spatial extremes