In view of the extensive development of the cerebellar hemispheres and the cerebral cortex
in mammals, it is natural to inquire whether some highly significant and unique functional …

… Such an estimation method may yield interesting network estimates, but as Allen and Liu72
note, these estimates do not correspond to a consistent joint density. Instead, the underlying …

New technologies have led to vast troves of large and complex data sets across many scientific
domains and industries. People routinely use machine learning techniques not only to …

Undirected graphical models, or Markov networks, are a popular class of statistical models,
used in a wide variety of applications. Popular instances of this class include Gaussian …

Motivation: Massive amounts of high-throughput genomics data profiled from tumor samples
were made publicly available by the Cancer Genome Atlas (TCGA). Results: We have …

… GI Allen is partially supported by NSF DMS-1209017, J. Taylor is partially supported by
NSF DMS-0906801, and L. Grosenick is supported by NSF IGERT Award #0801700. …

Missing data estimation is an important challenge with high-dimensional data arranged in
the form of a matrix. Typically this data matrix is transposable, meaning that either the rows, …

In the biclustering problem, we seek to simultaneously group observations and features.
While biclustering has applications in a wide array of domains, ranging from text mining to …

Gaussian graphical models, a class of undirected graphs or Markov Networks, are often
used to infer gene networks based on microarray expression data. Many scientists, however, …

Undirected graphical models, such as Gaussian graphical models, Ising, and multinomial/categorical
graphical models, are widely used in a variety of applications for modeling …