Working Groups of Statistical Interest

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Working groups in the Statistics Department

Working groups of statistical interest in other departments

Working groups in the Statistics Department

Empirical Processes working group:
Studies empirical process methods (inequalities, limit theorems, preservation theorems) and application of these to problems in statistical theory, including semiparametric models and machine learning.

Contacts: Jon A. Wellner

Model Based Clustering and Bayesian Model Section Working Group:
This working group meets weekly on Fridays at 9am in Padelford C-301. The main topics are:

  • model-based clustering and mixture models, with applications, including gene expression data and medical imaging;
  • Bayesian model selection via Bayes factors, and Bayesian model averaging; and
  • inference for deterministic simulation models, including numerical weather prediction, human population projections, and urban planning.

Contact: Adrian E. Raftery

Social Network Modeling Working Group:
The purpose of this working group is to developing statistical theory to guide inference for statistical modeling of network. The focus is on the structure of the models, and the design and analysis of surveys to collect network data. The latter is based on issues of inference in the presence of missing data.

We also focus on the design of statistical sampling from networks. Network sampling involves two units: nodes and links. While this can be thought of as a multi-level sampling design, the two levels are not nested in the traditional manner. We make systematic use of current network data to examine the information loss under alternative sampling strategies, and to develop the statistical theory for network sampling. In particular, we are develop the statistical theory and methods for network estimation based on partial network sampling designs.

This is a large collaborative project with many participants within UW and worldwide. The work is funded by the multiple grants from the NIH and NSF. We welcome new members.

Contacts: Martina Morris and Mark S. Handcock

Unsupervised Learning Working Group:

Contacts: Werner Stuetzle

Working groups of statistical interest in other departments

Graphical/Kernel Reading Group (GRG):
A weekly informal meeting of discussions of papers regarding graphical models and kernel methods, and their relatedness to statistics, machine learning, speech, langauge, and other time-series processing. We review papers from the machine learning (such as uncertainty in Artificial Intelligence, UAI, and Neural information processing systems, NIPS) and other relevant sources. Each quarter, we cover papers on a variety of topics ranging from graph theory, convexity theory, approximate inference, kernel machines, combinatorial optimization, and recent papers from this year's UAI/NIPS/ICML.

Contacts: Jeff Bilmes