Publications of Sarah Schott

%%    
@article{fds353798,
   Author = {Schott, S and Slate Young and E and Bookman, J and Peterson,
             B},
   Title = {Evaluating a Large-Scale Multi-Institution Project:
             Challenges Faced and Lessons Learned},
   Journal = {The Journal of Mathematics and Science: Collaborative
             Explorations (Jmsce)},
   Volume = {16},
   Number = {1},
   Year = {2020},
   Abstract = {SUMMIT-P consists of nine participating institutions working
             toward common goals but from unique perspectives. Evaluating
             such a large-scale project with diverse stakeholders has
             presented challenges. For one, evaluation on this scale
             necessitates a team effort rather than a single evaluator.
             Communication is key among the evaluators as well as among
             the project players at large. Participation and reliable,
             timely feedback from participants are perhaps the most
             important issues while also posing some of our greatest
             challenges. We present strategies we developed to counteract
             these challenges. In particular, we discuss the development
             of an assessment tracking system used to not only monitor
             responses but to also promote an increase in on-time
             responses. We conclude with a discussion of some lessons
             learned about evaluating large-scale, multi-site projects to
             share with other evaluators and PIs alike.},
   Doi = {10.25891/5e14-nf34},
   Key = {fds353798}
}

@article{fds296295,
   Author = {Bookman, and Bar-On, R and Cooke, B and Schott, S},
   Title = {(Re)discovering SoTL through a Fundamental Challenge:
             Helping Students Transition to College Calculus},
   Journal = {Maa Notes: Guide to the Scholarship of Teaching and Learning
             in Mathematics},
   Year = {2012},
   Month = {Fall},
   Key = {fds296295}
}

@article{fds296294,
   Author = {Huber, M and Schott, S},
   Title = {Random Construction of Interpolating Sets for High
             Dimensional Integration},
   Journal = {Journal of Applied Probability},
   Volume = {51},
   Number = {1},
   Pages = {92-105},
   Publisher = {Cambridge University Press (CUP)},
   Year = {2012},
   Abstract = {Computing the value of a high-dimensional integral can often
             be reduced to the problem of finding the ratio between the
             measures of two sets. Monte Carlo methods are often used to
             approximate this ratio, but often one set will be
             exponentially larger than the other, which leads to an
             exponentially large variance. A standard method of dealing
             with this problem is to interpolate between the sets with a
             sequence of nested sets where neighboring sets have relative
             measures bounded above by a constant. Choosing such a
             well-balanced sequence can rarely be done without extensive
             study of a problem. Here a new approach that automatically
             obtains such sets is presented. These well-balanced sets
             allow for faster approximation algorithms for integrals and
             sums using fewer samples, and better tempering and annealing
             Markov chains for generating random samples. Applications,
             such as finding the partition function of the Ising model
             and normalizing constants for posterior distributions in
             Bayesian methods, are discussed. © Applied Probability
             Trust 2014.},
   Doi = {10.1239/jap/1395771416},
   Key = {fds296294}
}

@article{fds296296,
   Author = {Schott, SJ},
   Title = {Girls in Math},
   Journal = {Encompass Magazine},
   Pages = {14-15},
   Year = {2011},
   Month = {April},
   url = {http://issuu.com/encompassmag/docs/encompass_sp11},
   Key = {fds296296}
}

@article{fds296297,
   Author = {Huber, M and Schott, S},
   Title = {Using TPA for Bayesian Inference},
   Journal = {Bayesian Statistics 9},
   Volume = {9780199694587},
   Pages = {257-282},
   Publisher = {Oxford Press},
   Year = {2010},
   url = {http://hdl.handle.net/10161/6637 Duke open access
             repository},
   Abstract = {Finding the integrated likelihood of a model given the data
             requires the integration of a nonnegative function over the
             parameter space. Classical Monte Carlo methods for numerical
             integration require a bound or estimate of the variance in
             order to determine the quality of the output. The method
             called the product estimator does not require knowledge of
             the variance in order to produce a result of guaranteed
             quality, but requires a cooling schedule that must have
             certain strict properties. Finding a cooling schedule can be
             difficult, and finding an optimal cooling schedule is
             usually computationally out of reach. TPA is a method that
             solves this difficulty, creating an optimal cooling schedule
             automatically as it is run. This method has its own set of
             requirements; here it is shown how to meet these
             requirements for problems arising in Bayesian inference.
             This gives guaranteed accuracy for integrated likelihoods
             and posterior means of nonnegative parameters.},
   Doi = {10.1093/acprof:oso/9780199694587.003.0009},
   Key = {fds296297}
}