research
∙
05/25/2022
Clustering consistency with Dirichlet process mixtures
Dirichlet process mixtures are flexible non-parametric models, particula...
share
research
∙
04/27/2022
Smoothing distributions for conditional Fleming-Viot and Dawson-Watanabe diffusions
We study the distribution of the unobserved states of two measure-valued...
research
∙
03/26/2022
Model Selection for Maternal Hypertensive Disorders with Symmetric Hierarchical Dirichlet Processes
Hypertensive disorders of pregnancy occur in about 10 around the world. ...
research
∙
01/18/2022
Flexible clustering via hidden hierarchical Dirichlet priors
The Bayesian approach to inference stands out for naturally allowing bor...
research
∙
10/19/2021
BNPdensity: Bayesian nonparametric mixture modeling in R
Robust statistical data modelling under potential model mis-specificatio...
research
∙
09/14/2021
A Wasserstein index of dependence for random measures
Nonparametric latent structure models provide flexible inference on dist...
research
∙
07/21/2021
Inner spike and slab Bayesian nonparametric models
Discrete Bayesian nonparametric models whose expectation is a convex lin...
research
∙
01/27/2020
Predictive inference with Fleming–Viot-driven dependent Dirichlet processes
We consider predictive inference using a class of temporally dependent D...
research
∙
01/25/2018
Limiting behaviour of the stationary search cost distribution driven by a generalized gamma process
Consider a list of labeled objects that are organized in a heap. At each...
research
∙
01/15/2018
