Rejoinder: Classifier Technology and the Illusion of Progress

Rejoinder: Classifier Technology and the Illusion of Progress [math.ST/0606441]Comment: Published at http://dx.doi.org/10.1214/088342306000000079 in the Statistical Science (http://www.imstat.org/sts/) by the Institute of Mathematical Statistics (http://www.imstat.org

Discussion of "Bayesian Models and Methods in Public Policy and Government Settings" by S. E. Fienberg

Fienberg convincingly demonstrates that Bayesian models and methods represent a powerful approach to squeezing illumination from data in public policy settings. However, no school of inference is without its weaknesses, and, in the face of the ambiguities, uncertainties, and poorly posed questions of the real world, perhaps we should not expect to find a formally correct inferential strategy which can be universally applied, whatever the nature of the question: we should not expect to be able to identify a "norm" approach. An analogy is made between George Box's "no models are right, but some are useful," and inferential systems [arXiv:1108.2177].Comment: Published in at http://dx.doi.org/10.1214/11-STS331A the Statistical Science (http://www.imstat.org/sts/) by the Institute of Mathematical Statistics (http://www.imstat.org

Bayesian anomaly detection methods for social networks

Learning the network structure of a large graph is computationally demanding, and dynamically monitoring the network over time for any changes in structure threatens to be more challenging still. This paper presents a two-stage method for anomaly detection in dynamic graphs: the first stage uses simple, conjugate Bayesian models for discrete time counting processes to track the pairwise links of all nodes in the graph to assess normality of behavior; the second stage applies standard network inference tools on a greatly reduced subset of potentially anomalous nodes. The utility of the method is demonstrated on simulated and real data sets.Comment: Published in at http://dx.doi.org/10.1214/10-AOAS329 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

Finding Groups in Gene Expression Data

The vast potential of the genomic insight offered by microarray technologies has led to their widespread use since they were introduced a decade ago. Application areas include gene function discovery, disease diagnosis, and inferring regulatory networks. Microarray experiments enable large-scale, high-throughput investigations of gene activity and have thus provided the data analyst with a distinctive, high-dimensional field of study. Many questions in this field relate to finding subgroups of data profiles which are very similar. A popular type of exploratory tool for finding subgroups is cluster analysis, and many different flavors of algorithms have been used and indeed tailored for microarray data. Cluster analysis, however, implies a partitioning of the entire data set, and this does not always match the objective. Sometimes pattern discovery or bump hunting tools are more appropriate. This paper reviews these various tools for finding interesting subgroups

Note on parallel universes

The parallel universes idea is an attempt to integrate several aspects of learning which share some common aspects. This is an interesting idea: if successful, insights could cross-fertilise, leading to advances in each area. The "multi-view" perspective seems to us to have particular potential