Overweight and obesity in adults are known to be associated with increased risk of
metabolic and cardiovascular diseases. Obesity has now reached epidemic proportions …

We introduce the nonparametric metadata dependent relational (NMDR) model, a Bayesian
nonparametric stochastic block model for network data. The NMDR allows the entities …

Hippocampal volume is one of the best established biomarkers for Alzheimer's disease.
However, for appropriate use in clinical trials research, the evolution of hippocampal volume …

A new Le ́vy process prior is proposed for an uncountable collection of covariate-dependent
feature-learning measures; the model is called the kernel beta process (KBP). Available …

We propose a novel Bayesian nonparametric process prior for modeling a collection of
random discrete distributions. This process is defined by including a suitable Beta …

Spectral clustering views the similarity matrix as a weighted graph, and partitions the data by
minimizing a graph-cut loss. Since it minimizes the across-cluster similarity, there is no need …

Given an audio signal with an unknown number of people speaking, speaker diarization
aims to automatically answer the question “who spoke when.” Crucial to the success of …

Dependent Species Sampling Models for Spatial Density Estimation Page 1 Bayesian
Analysis (2017) 12, Number 2, pp. 379–406 Dependent Species Sampling Models for Spatial …

This paper examines the use of Dirichlet process mixtures for curve fitting. An important
modelling aspect in this setting is the choice between constant and covariate‐dependent …

We present a mixed multinomial logit (MNL) model, which leverages the truncated stick-
breaking process representation of the Dirichlet process as a flexible nonparametric mixing …