%%
@article{fds353798,
Author = {Schott, S and Slate Young and E and Bookman, J and Peterson,
B},
Title = {Evaluating a LargeScale MultiInstitution Project:
Challenges Faced and Lessons Learned},
Journal = {The Journal of Mathematics and Science: Collaborative
Explorations (Jmsce)},
Volume = {16},
Number = {1},
Year = {2020},
url = {http://dx.doi.org/10.25891/5e14nf34},
Abstract = {SUMMITP consists of nine participating institutions working
toward common goals but from unique perspectives. Evaluating
such a largescale 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 ontime
responses. We conclude with a discussion of some lessons
learned about evaluating largescale, multisite projects to
share with other evaluators and PIs alike.},
Doi = {10.25891/5e14nf34},
Key = {fds353798}
}
@article{fds296295,
Author = {Bookman, and BarOn, 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 = {92105},
Publisher = {Cambridge University Press (CUP)},
Year = {2012},
url = {http://dx.doi.org/10.1239/jap/1395771416},
Abstract = {Computing the value of a highdimensional 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
wellbalanced sequence can rarely be done without extensive
study of a problem. Here a new approach that automatically
obtains such sets is presented. These wellbalanced 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 = {1415},
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 = {257282},
Publisher = {Oxford Press},
Year = {2010},
url = {http://hdl.handle.net/10161/6637 Duke open
access},
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}
}
