In complex systems it often occurs that relevant information about the system state and behavior is provided by groups of low-level variables rather than single variables. This grouping into high-level variables introduces a hierachy in the knowledge that can only be captured by means of rules involving propositions with a representation capability that is more complex than usual ones. In this ...

Gaussian latent factor models are routinely used for modeling of dependence in continuous, binary, and ordered categorical data. For unordered categorical variables, Gaussian latent factor models lead to challenging computation and complex modeling structures. As an alternative, we propose a novel class of simplex factor models. In the single-factor case, the model treats the different categori...

When product quality characteristics are evaluated and assigned to exclusive categories, measurement errors (misclassification of products) always exist unless a perfect measurement system is used to identify the categories. In run-to-run (R2R) process control, a categorical controller has been developed for process adjustments with categorical variables. However, if process outputs are misclas...

This paper provides an introduction to a recently developed conceptual framework-the dimensional-categorical spectrum-for utilizing general factor mixture models to explore the latent structures of psychological constructs. This framework offers advantages over traditional latent variable models that usually employ either continuous latent factors or categorical latent class variables to charac...

In this tutorial we show how complete hierarchical multinomial marginal (HMM) models for categorical variables can be defined, estimated and tested using the hmmm package.

In the optimization of decision diagrams, variable reordering approaches are often used to minimize the number of nodes. However, such approaches are less effective for analysis of multi-state systems given by monotone structure functions. Thus, in this paper, we propose algorithms to minimize the number of edges in an edge-valued multi-valued decision diagram (EVMDD) for fast analysis of multi...

Paper describes two GIS methods for visualisation of uncertainty associated with spatial prediction of continuous and categorical variables. In the case of continuous variables, the key issue is to visualise both predictions and the prediction error at the same time, while in the case of categorical data, the key issue is to visualise multiple memberships and confusion in-between them. Both met...