A Common Atom Model for the Bayesian Nonparametric Analysis of Nested Data

by   Francesco Denti, et al.

The use of high-dimensional data for targeted therapeutic interventions requires new ways to characterize the heterogeneity observed across subgroups of a specific population. In particular, models for partially exchangeable data are needed for inference on nested datasets, where the observations are assumed to be organized in different units and some sharing of information is required to learn distinctive features of the units. In this manuscript, we propose a nested Common Atoms Model (CAM) that is particularly suited for the analysis of nested datasets where the distributions of the units are expected to differ only over a small fraction of the observations sampled from each unit. The proposed CAM allows a two-layered clustering at the distributional and observational level and is amenable to scalable posterior inference through the use of a computationally efficient nested slice-sampler algorithm. We further discuss how to extend the proposed modeling framework to handle discrete measurements, and we conduct posterior inference on a real microbiome dataset from a diet swap study to investigate how the alterations in intestinal microbiota composition are associated with different eating habits. We further investigate the performance of our model in capturing true distributional structures in the population by means of a simulation study.



There are no comments yet.


page 18


Latent nested nonparametric priors

Discrete random structures are important tools in Bayesian nonparametric...

Flexible clustering via hidden hierarchical Dirichlet priors

The Bayesian approach to inference stands out for naturally allowing bor...

Separate Exchangeability as Modeling Principle in Bayesian Nonparametrics

We argue for the use of separate exchangeability as a modeling principle...

Bayesian nonparametric analysis for the detection of spikes in noisy calcium imaging data

Recent advancements in miniaturized fluorescence microscopy have made it...

Nested sampling with any prior you like

Nested sampling is an important tool for conducting Bayesian analysis in...

Inference of global clusters from locally distributed data

We consider the problem of analyzing the heterogeneity of clustering dis...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.