Name
Abstract | ||
|---|---|---|
Estimating the parameters of mathematical models is a common problem in almost all branches of science. However, this problem can prove notably difficult when processes and model descriptions become increasingly complex and an explicit likelihood function is not available. With this work, we propose a novel method for globally amortized Bayesian inference based on invertible neural networks that w... |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1109/TNNLS.2020.3042395 | IEEE Transactions on Neural Networks and Learning Systems |
Keywords | DocType | Volume |
Data models,Bayes methods,Biological system modeling,Neural networks,Training,Numerical models,Estimation | Journal | 33 |
Issue | ISSN | Citations |
4 | 2162-237X | 0 |
PageRank | References | Authors |
0.34 | 10 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Stefan T Radev | 1 | 2 | 1.07 |
| Ulf K Mertens | 2 | 0 | 0.34 |
| Andreas Voss | 3 | 8 | 7.10 |
| Lynton Ardizzone | 4 | 3 | 4.47 |
| Ullrich Koethe | 5 | 249 | 22.37 |