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Maximilian Scholz
Maximilian Scholz
University of Stuttgart - Cluster of Excellence SimTech
Verified email at simtech.uni-stuttgart.de - Homepage
Title
Cited by
Cited by
Year
Some models are useful, but how do we know which ones? Towards a unified Bayesian model taxonomy
PC Bürkner, M Scholz, ST Radev
Statistic Surveys 17, 216-310, 2023
142023
An empirical study of Linespots: A novel past‐fault algorithm
M Scholz, R Torkar
Software Testing, Verification and Reliability 31 (8), e1787, 2021
52021
Prediction can be safely used as a proxy for explanation in causally consistent Bayesian generalized linear models
M Scholz, PC Bürkner
arXiv preprint arXiv:2210.06927, 2022
42022
Posterior accuracy and calibration under misspecification in Bayesian generalized linear models
M Scholz, PC Bürkner
arXiv preprint arXiv:2311.09081, 2023
2023
An Analysis of Linespots and its Utility in Realistic Scenarios
M Scholz
Chalmers University of Technology, 2019
2019
A Line Based Approach for Bugspots
M Scholz
2016
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Articles 1–6