We consider nested sequences of hierarchical loglinear models when expected frequencies are subject to linear constraints and we study the problem of finding the model in the the nested sequence that is able to explain more clearly the given data. It will be necessary to give a method to estimate the parameters of the loglinear models and also a procedure to choose the best model among the mode...

Neural spike trains, which are sequences of very brief jumps in voltage across the cell membrane, were one of the motivating applications for the development of point process methodology. Early work required the assumption of stationarity, but contemporary experiments often use time-varying stimuli and produce time-varying neural responses. More recently, many statistical methods have been deve...

Loglinear modeling methods have become quite straightforward to apply to discrete data X. A good-fitting loglinear model can be used to generate synthetic copies of X1, ..., Xn of X that preserve analytic properties but may allow reidentification of small cells. With fitting algorithms that use more general convex constraints and are designed to deal with missing data, we are able to disperse t...

Recent work on loglinear models in probabilistic constraint logic programming is applied to firstorder probabilistic reasoning. Probabilities are defined directly on the proofs of atomic formulae, and by marginalisation on the atomic formulae themselves. We use Stochastic Logic Programs (SLPs) composed of labelled and unlabelled definite clauses to define the proof probabilities. We have a cons...

We investigate the influence of information status (IS) on constituent order in German, and integrate our findings into a loglinear surface realisation ranking model. We show that the distribution of pairs of IS categories is strongly asymmetric. Moreover, each category is correlated with morphosyntactic features, which can be automatically detected. We build a loglinear model that incorporates...

Portuguese dictionaries lack information about the kind of phrases or clauses a verb goes with. In this paper we describe how this information can be computationally learned from an automatically tagged corpus with almost 10,000,000 words. Loglinear modeling for categorical data will be used to analyze and automatically identify the subcatego-rization dependencies. Loglinear models were also us...