How can we find the right graph for semi-supervised learning? In real world applications, the
choice of which edges to use for computation is the first step in any graph learning process …

The goal of clustering is to detect the presence of distinct groups in a dataset and assign
group labels to the observations. Nonparametric clustering is based on the premise that the …

The integrative personal omics profile (iPOP) is a pioneering study that combines genomics,
transcriptomics, proteomics, metabolomics and autoantibody profiles from a single individual …

Discrete Markov random fields are undirected graphical models in which the nodes of a
graph are discrete random variables with values usually represented by colors. Typically …

Motivation: Clustering of chemical and biochemical data based on observed features is a
central cognitive step in the analysis of chemical substances, in particular in combinatorial …

Aiming at the utilization of adjacent image correlation information in multi-target
segmentation of 3D image slices and the optimization of segmentation results, a 3D grouped …

Supplement to “The Gibbs-plaid biclustering model”. A high-resolution version of the image
shown in Figure 6, as well as the complete biclustering results associated with the RD data …

A Bayesian semi-parametric survival regression model with latent partitions is introduced. Its
goal is to predict survival and to cluster survival patients within the context of building …

This article presents a Bayesian kernel-based clustering method. The associated model
arises as an embedding of the Potts density for class membership probabilities into an …

We consider Bayesian nonparametric regression through random partition models. Our
approach involves the construction of a covariate-dependent prior distribution on partitions …