Today's data-heavy research environment requires the integration of different
sources of information into structured data sets that can not be analyzed as
simple matrices.
Many casinos routinely use mechanical card shuffling machines. We were asked
to evaluate a new product, a shelf shuffler. This leads to new probability, new
combinatorics, and to some practical advice which was adopted by the
manufacturer. The interplay between theory, computing, and real-world
application is developed.
Inferential summaries of tree estimates are useful in the setting of
evolutionary biology, where phylogenetic trees have been built from DNA data
since the 1960's. In bioinformatics, psychometrics and data mining,
hierarchical clustering techniques output the same mathematical objects, and
practitioners have similar questions about the stability and `generalizability'
of these summaries.
We provide an explanation of the main ideas underlying G\"otze's main result
in using Stein's method. We also provide detailed derivations of various
intermediate estimates. Curiously, we are led to a different dimensional
dependence of the constant than that given G\"otze's paper. We would like to
dedicate this to Charles Stein on the occasion of his 90th birthday.
We study the limit theory of large threshold graphs and apply this to a
variety of models for random threshold graphs. The results give a nice set of
examples for the emerging theory of graph limits.