18 citations · 19 across the 2 of their papers we have counts for
3 papers
stat.ME2022★ 1 cited
Prediction can be safely used as a proxy for explanation in causally consistent Bayesian generalized linear models
Maximilian Scholz, Paul-Christian Bürkner
Bayesian modeling provides a principled approach to quantifying uncertainty in model parameters and model structure and has seen a surge of applications in recent years. Within the…
stat.ME2022★ 18 cited
Some models are useful, but how do we know which ones? Towards a unified Bayesian model taxonomy
Paul-Christian Bürkner, Maximilian Scholz, Stefan T. Radev
Probabilistic (Bayesian) modeling has experienced a surge of applications in almost all quantitative sciences and industrial areas. This development is driven by a combination of s…
cs.SE2020
An empirical study of Linespots: A novel past-fault algorithm
Maximilian Scholz, Richard Torkar
This paper proposes the novel past-faults fault prediction algorithm Linespots, based on the Bugspots algorithm. We analyze the predictive performance and runtime of Linespots comp…