Statistical modeling is a powerful tool for developing and testing theories by way of causal explanation, prediction, and description. In many disciplines there is near-exclusive use of statistical modeling for causal explanation and the assumption that models with high explanatory power are inherently of high predictive power. Conflation between explanation and prediction is common, yet the di...

In the context of the Grand Unified MSSM, we investigate the fermion mass matrices at GUT scale. We note that from the experimental mass pattern the determinants of the Yukawa matrices at this scale can be unified with good precision. Taking the unification o determinants as an hypothesis, it gives two model independent predictions that in the MSSM turns out to determine an appropriate value fo...

One important function of the discussion forums of Massive Open Online Courses (MOOCs) is for students to post problems they are unable to resolve and receive help from their peers and instructors. There are a large proportion of threads that are not resolved to the satisfaction of the students for various reasons. In this paper, we attack this problem by firstly constructing a conceptual model...

Financial institutions use quantitative risk models not only to manage their risks, but also to communicate information. The Basel regulation in particular uses banks’ own estimates to make capital requirements more sensitive to each bank’s risks, and both the models and the regulation have been blamed for their over-optimism. I link over-optimism to a hidden information problem between a regul...

Database researchers, statisticians, and \data owners" often have quite di erent views of data. In a database context the traditional goal has been to provide a general and exible data management framework, with less concern about the content of the data. Statisticians on the other hand have traditionally focused primarily on issues of data modeling and inference with relatively little concern ...