O Dürr, PY Fan, ZX Yin - IEEE Sensors Journal, 2023
Abstract: This study aims to investigate the utilization of Bayesian techniques for the calibration of micro-electro-mechanical system (MEMS) accelerometers. These devices have garnered substantial interest in various practical applications and typically require calibration...
D Dold, D Rügamer, B Sick, O Dürr - International Conference on Artificial Intelligence and Statistics, 2024
Abstract: Semi-structured regression models enable the joint modeling of interpretable structured and complex unstructured feature effects. The structured model part is inspired by statistical models and can...
O Dürr, PY Fan, ZX Yin - IEEE Sensors Journal, 2023
Abstract: This study aims to investigate the utilization of Bayesian techniques for the calibration of micro-electro-mechanical system (MEMS) accelerometers. These devices have garnered substantial interest in various practical applications and typically require calibration...
C Berlin, S Adomeit, P Grover, M Dreischarf, H Halm, O Dürr, P Obid - Global Spine Journal, 2024
Study design: Retrospective, mono-centric cohort research study. Objectives:
The purpose of this study is to validate a novel artificial intelligence (AI)-based algorithm against human-generated ground truth...
L Kook, PFM Baumann, O Dürr, B Sick, D Rügamer - arXiv preprint arXiv:2211.13665, 2022
Abstract: Contemporary empirical applications frequently require flexible regression models for complex response types and large tabular or non-tabular, including image or text, data. Classical regression models either break down...
L Herzog, L Kook, A Götschi, K Petermann, M Hänsel, J Hamann, O Dürr, S Wegener, B Sick - Biometrical Journal, 2023
Abstract: In many medical applications, interpretable models with high prediction performance are sought. Often, those models are required to handle semistructured data...
M Arpogaus, M Voss, B Sick, M Nigge-Uricher, O Dürr - IEEE Transactions on Smart Grid, 2023
Abstract: The transition to a fully renewable energy grid requires better forecasting of demand at the low-voltage level to increase efficiency and ensure reliable control. However, high...
O Dürr, S Hörtling, D Dold, I Kovylov, B Sick - AStA Advances in Statistical Analysis, 2024
Abstract: Black-box variational inference (BBVI) is a technique to approximate the posterior of Bayesian models by optimization. Similar to MCMC, the user only needs to specify the...
O Dürr, B Sick, E Murina - 2020
Abstract: Probabilistic Deep Learning is a hands-on guide to the principles that support neural
networks. Learn to improve network performance with the right distribution for different data
types, and discover Bayesian variants that can state their own uncertainty...
L Kook, L Herzog, T Hothorn, O Dürr, B Sick - Pattern Recognition - 2022•Elsevier
Abstract: Outcomes with a natural order commonly occur in prediction problems and often the available input data are a mixture of complex data like images and tabular predictors. Deep...