Underlying Distributions in Loglinear Models of Discrete Data

Underlying Distributions in Loglinear Models of Discrete Data

A conjugate prior for discrete hierarchical loglinear models

In Bayesian analysis of multi-way contingency tables, the selection of a prior distribution for either the log-linear parameters or the cell probabilities parameters is a major challenge. In this paper, we define a flexible family of conjugate priors for the wide class of discrete hierarchical log-linear models, which includes the class of graphical models. These priors are defined as the Diaco...

Negative Binomial Loglinear Mixed Models

Testing Probability Distributions Underlying Aggregated Data

In this paper, we analyze and study a hybrid model for testing and learning probability distributions. Here, in addition to samples, the testing algorithm is provided with one of two different types of oracles to the unknown distribution D over [n]. More precisely, we define both the dual and cumulative dual access models, in which the algorithm A can both sample from D and respectively, for an...

Spatial scan statistics in loglinear models

The likelihood ratio spatial scan statistic has been widely used in spatial disease surveillance and spatial cluster detection applications. In order to better understand cluster mechanisms, an equivalent model-based approach is proposed to the spatial scan statistic that unifies currently loosely coupled methods for including ecological covariates in the spatial scan test. In addition, the uti...