Bayesian model parameter learning in linear inverse problems: application in EEG focal source imaging
Inverse problems are often described as limited-data problems in which the signal of interest cannot be observed directly. Therefore, a physics-based forward model that relates the signal with the observations is typically needed. Unfortunately, unknown model parameters and imperfect forward models...
Saved in:
| Main Authors: | Alexandra Koulouri, Ville Rimpiläinen |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IOP Publishing
2025-01-01
|
| Series: | Machine Learning: Science and Technology |
| Subjects: | |
| Online Access: | https://doi.org/10.1088/2632-2153/aded57 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Comparative study on inversion of the unsaturated hydraulic parameters using optimization and Bayesian estimation methods
by: KE Fengqiao, et al.
Published: (2016-09-01) -
Bayesian Structure Learning and Sampling of Bayesian Networks with the R Package BiDAG
by: Polina Suter, et al.
Published: (2023-01-01) -
Federated Bayesian Deep Learning: The Application of Statistical Aggregation Methods to Bayesian Models
by: John Fischer, et al.
Published: (2024-01-01) -
Bayesian estimation of the relative deviations between activities in the radionuclide standardization
by: Fabio Ludolf Cacais, et al.
Published: (2019-10-01) -
Feature-aware domain invariant representation learning for EEG motor imagery decoding
by: Jianxiu Li, et al.
Published: (2025-03-01)