— Probabilistic models are widely used to analyze embedded, networked, and more recently biological systems. Existing numerical analysis techniques are limited to finitestate mo...
We give a fast and practical algorithm for statistical learning hyperparameters from observable data in probabilistic image processing, which is based on Gaussian graphical model ...
In this paper, we present a novel approach for authorship attribution, the task of identifying the author of a document, using probabilistic context-free grammars. Our approach in...
Sindhu Raghavan, Adriana Kovashka, Raymond J. Moon...
We present a class of models that, via a simple construction,
enables exact, incremental, non-parametric, polynomial-time,
Bayesian inference of conditional measures. The approac...
The paper studies the relationship between logic programs with the stable model semantics and difference logic recently considered in the Satisfiability Modulo Theories framework....