In the context of medically relevant artificial intelligence, many real-world problems involve both continuous and categorical feature variables. When the data are mixed mode, the assumption of multivariate Gaussian distributions for the gating network of normalized Gaussian (NG) expert networks, such as NG mixture of experts (NGME), becomes invalid. An independence model has been studied to ha...

A new method is described for determining how the visual system resolves ambiguities in the compositional structure of multi-surface objects; i.e., how the surfaces of objects are grouped together to form a hierarchical structure. The method entails dynamic grouping motion, a high level process in which changes in a surface (e.g., increases or decreases in its luminance, hue or texture) transie...

We propose an easy-to-use methodology to allocate one of the groups which have been previously built from a complete learning data base, to new individuals. The learning data base contains continuous and categorical variables for each individual. The groups (clusters) are built by using only the continuous variables and described with the help of the categorical ones. For the new individuals, o...

Almost every elementary statistics textbook has some coverage of the chi-square test (e. g., Comrey & Lee, 2007, Kirk, 2007, Howell, 2002). In particular, the chi-square test is presented in the analysis of categorical data. Most of these textbooks will take the reader up to the contingency table that involves the cross tabulation of two categorical variables. With contingency tables, there are...

A scant few weeks ago disentanglement of effects identified in purely categorical designs in which all variables are categorical, including notoriously-complex rectangular categorical designs (RCDs) in which variables have a different number of response categories, was poorly understood. However, univariate and multivariate optimal (“maximum-accuracy”) statistical methods, specifically UniODA a...