Ben Baumer

TL;DR: In this article, the importance of data science proficiency and resources for instructors to implement data science in their own statistics curricula are discussed. But these data science topics have not traditionally been a major component of undergraduate programs in statistics.

TL;DR: This work focuses on developing a linear time algorithm that maximizes the expected lifetime under a random uniform model of sensor distribution, and demonstrates one such algorithm that achieves an average-case approximation ratio of almost 0.9.

TL;DR: Two algorithms are proposed that exploit the edge weight distribution in the input network and produce performance gains of more than 3 orders of magnitude compared to an exhaustive solution and one guarantees a constant factor approximation while the other scales to large and dense networks without compromising the solution quality.

TL;DR: It is proved that batting average depends more heavily upon a particularly unpredictable variable, hits per balls in play (HPBP), than does OBP, which will explain why for both batters and pitchers, on-base percentage is a better indicator of future performance than batting average.

TL;DR: This work focuses on developing an efficient algorithm that maximizes the expected network lifetime under a random uniform model of sensor distribution, and demonstrates one such algorithm that achieves an expected network Lifetime within 12 % of the theoretical maximum.