Proper handling of continuous variables is crucial in healthcare research, for example, within regression modelling for descriptive, explanatory, or predictive purposes. However, inadequate methods ...

For example, BERT-based masked language models (MLMs) can be trained in a self-supervised way on hundreds of millions to billions of readily available SMILES strings. Another option is graph neural ...

This article considers a graphical model for ordinal variables, where it is assumed that the data are generated by discretizing the marginal distributions of a latent multivariate Gaussian ...

This is a graph context-aware diffusion model that utilizes multimodal graph information. This approach addresses graph space complexity by compressing contexts from graphs into fixed capacity graph ...

This paper shows that the Expectation-Maximization (EM) algorithm for regime-switching dynamic factor models provides satisfactory performance relative to other estimation methods and delivers a good ...

Factor graph, as a bipartite graphical model, offers a structured representation by revealing local connections among graph nodes. This study explores the utilization of factor graphs in modeling the ...

This paper develops a new approach to estimating the degree of informality in an economy. It combines direct yet infrequent measures of the informal economy in micro data with an augmented factor ...

#14 Factor graphs Set by Katie Steckles To construct a factor graph, we dot numbers around a page and draw lines between pairs where one is divisible by the other.

Many models with varying degrees of complexity have been developed to assess changes in tumor size. Some are static models, first modeling tumor kinetics and then using estimated kinetic parameters as ...