A Dependent Feature Allocation Model Based on Random Fields
Fuente:
arXiv
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866908722974949376 |
|---|---|
| author | Flores, Bernardo Ni, Yang Xu, Yanxun Müller, Peter |
| author_facet | Flores, Bernardo Ni, Yang Xu, Yanxun Müller, Peter |
| contents | We introduce a flexible framework for modeling dependent feature allocations. Our approach addresses limitations in traditional nonparametric methods by directly modeling the logit-probability surface of the feature paintbox, enabling the explicit incorporation of covariates and complex but tractable dependence structures. The core of our model is a Gaussian Markov Random Field (GMRF), which we use to robustly decompose the latent field, separating a structural component based on the baseline covariates from intrinsic, unstructured heterogeneity. This structure is not a rigid grid but a sparse k-nearest neighbors graph derived from the latent geometry in the data, ensuring high-dimensional tractability. We extend this framework to a dynamic spatio-temporal process, allowing item effects to evolve via an Ornstein-Uhlenbeck process. Feature correlations are captured using a low-rank factorization of their joint prior. We demonstrate our model's utility by applying it to a polypharmacy dataset, successfully inferring latent health conditions from patient drug profiles. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_17701 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Dependent Feature Allocation Model Based on Random Fields Flores, Bernardo Ni, Yang Xu, Yanxun Müller, Peter Methodology We introduce a flexible framework for modeling dependent feature allocations. Our approach addresses limitations in traditional nonparametric methods by directly modeling the logit-probability surface of the feature paintbox, enabling the explicit incorporation of covariates and complex but tractable dependence structures. The core of our model is a Gaussian Markov Random Field (GMRF), which we use to robustly decompose the latent field, separating a structural component based on the baseline covariates from intrinsic, unstructured heterogeneity. This structure is not a rigid grid but a sparse k-nearest neighbors graph derived from the latent geometry in the data, ensuring high-dimensional tractability. We extend this framework to a dynamic spatio-temporal process, allowing item effects to evolve via an Ornstein-Uhlenbeck process. Feature correlations are captured using a low-rank factorization of their joint prior. We demonstrate our model's utility by applying it to a polypharmacy dataset, successfully inferring latent health conditions from patient drug profiles. |
| title | A Dependent Feature Allocation Model Based on Random Fields |
| topic | Methodology |
| url | https://arxiv.org/abs/2512.17701 |