We present an inference algorithm for perturbation models based on Poisson regression. The algorithm is designed to handle unclassified input with multiple errors described by independent mal-rules. This knowledge representation provides an intelligent tutoring system with local and global information about a student, such as error classification (local) and prediction of further performance (g...

This paper studies issues relating to the parameterization of probability distributions over binary data sets. Several such parameterizations of models for binary data are known, including the Ising, generalized Ising, canonical and full parameterizations. We also discuss a parameterization that we call the “spectral parameterization”, which has received significantly less coverage in existing ...

background : in medical studies, when the joint prediction about occurrence of two events should be anticipated, a statistical bivariate model is used. due to the limitations of usual statistical models, other methods such as artificial neural network (ann) and hybrid models could be used. in this paper, we propose a hybrid artificial neural network-genetic algorithm (ann-ga) model to predictio...

Background: The effective use of limited health care resources is of prime importance. Assessing the length of stay (LOS) is especially important in organizing hospital services and health system. This study was conducted to identify predictors of LOS among patients who were admitted to a general surgical unit.    Methods: In this cross-sectional study, the sample included all patien...