Traditional statistical methods for confidentiality protection of statistical
databases do not scale well to deal with GWAS (genome-wide association studies)
databases especially in terms of guarantees regarding protection from linkage
to external information. The more recent concept of differential privacy,
introduced by the cryptographic community, is an approach which provides a
rigorous definition of privacy with meaningful privacy guarantees in the
presence of arbitrary external information, although the guarantees come at a
serious price in terms of data utility.

Starting with the neo-Bayesian revival of the 1950s, many statisticians
argued that it was inappropriate to use Bayesian methods, and in particular
subjective Bayesian methods in governmental and public policy settings because
of their reliance upon prior distributions. But the Bayesian framework often
provides the primary way to respond to questions raised in these settings and
the numbers and diversity of Bayesian applications have grown dramatically in
recent years.

We study maximum likelihood estimation in log-linear models under conditional
Poisson sampling schemes. We derive necessary and sufficient conditions for
existence of the maximum likelihood estimator (MLE) of the model parameters and
investigate estimability of the natural and mean-value parameters under a
non-existent MLE. Our conditions focus on the role of sampling zeros in the
observed table. We situate our results within the general framework of extended
exponential families and we rely in a fundamental way on key geometric
properties of log-linear models.

Introduction to papers on the modeling and analysis of network data