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Dealing with Interference on Experimentation Platforms
(2018-09-16)
The theory of causal inference, as formalized by the potential outcomes framework, relies on an assumption that the experimental units are independent. When independence is not tenable, we say there is interference, and ...
Modeling Musical Influence Through Data
(2018-06-29)
Musical influence is a topic of interest and debate among critics, historians, and general listeners alike, yet to date there has been limited work done to tackle the subject in a quantitative way. In this thesis, we address ...
Advances in Monte Carlo Variational Inference and Applied Probabilistic Modeling
(2018-05-10)
Galvanized by the accelerated pace and ease of data collection, researchers in more and more disciplines are turning to large, heterogeneous datasets to answer scientific questions. Divining insight from massive and complex ...
Detecting Meaningful Relationships in Large Data Sets
(2018-05-01)
As data sets grow and algorithms scale, two questions have become central to data-rich science. The first is the exploration question: how can we avoid only testing hypotheses consistent with current models and instead ...
Figure Skating Scores: Prediction and Assessing Bias
(2018-06-29)
Figure skating has not yet benefited from the statistics craze surrounding America's most popular sports. The literature that does analyze figure skating largely deals with the scoring system abandoned in 2004. This thesis ...
Coding Be eR: Assessing and Improving the Reproducibility of R-Based Research With containR
(2018-06-29)
Reproducibility is the cornerstone of science, and we are in the midst of a reproducibility crisis. Simply sharing the code and data used for obtaining results is o en insu cient for reproducibility; in fact, we show that ...