Bayesian Sparse Covariance Structure Analysis for Correlated Count Data

2020-06-05 · Sho Ichigozaki, Takahiro Kawashima, Hayaru Shouno

In this paper, we propose a Bayesian Graphical LASSO for correlated countable data and apply it to spatial crime data. In the proposed model, we assume a Gaussian Graphical Model for the latent variables which dominate the potential risks of crimes. To evaluate the proposed model, we determine optimal hyperparameters which represent samples better. We apply the proposed model for estimation of the sparse inverse covariance of the latent variable and evaluate the partial correlation coefficients. Finally, we illustrate the results on crime spots data and consider the estimated latent variables and the partial correlation coefficients of the sparse inverse covariance.

📄 PDF Abstract BibTeX arXiv:2006.03241

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Sparse Covariance Estimation in Logit Mixture Models

2020-01-14 · Youssef M. Aboutaleb, Mazen Danaf, Yifei Xie, Moshe Ben-Akiva

This paper introduces a new data-driven methodology for estimating sparse covariance matrices of the random coefficients in logit mixture models. Researchers typically specify covariance matrices in logit mixture models …

A nonparametric variable clustering model

2012-12-01 · NeurIPS 2012 12 · Konstantina Palla, Zoubin Ghahramani, David A. Knowles

Factor analysis models effectively summarise the covariance structure of high dimensional data, but the solutions are typically hard to interpret. This motivates attempting to find a disjoint partition, i.e. a clustering…

Clustering model

Sparse Regression under Correlation and Weak Signals: A Reproducible Benchmark of Classical and Bayesian Methods

2026-04-04 · Hao Xiao arxiv

Choosing between classical and Bayesian sparse regression methods involves a real trade-off: penalized estimators like Lasso run in milliseconds but give no uncertainty estimates,while Horseshoe and Spike-and-Slab priors…

Mining Block I/O Traces for Cache Preloading with Sparse Temporal Non-parametric Mixture of Multivariate Poisson

2014-10-13 · Lavanya Sita Tekumalla, Chiranjib Bhattacharyya

Existing caching strategies, in the storage domain, though well suited to exploit short range spatio-temporal patterns, are unable to leverage long-range motifs for improving hitrates. Motivated by this, we investigate n…

Clustering

High-Dimensional Sparse Bayesian Learning without Covariance Matrices

2022-02-25 · Alexander Lin, Andrew H. Song, Berkin Bilgic, Demba Ba

Sparse Bayesian learning (SBL) is a powerful framework for tackling the sparse coding problem. However, the most popular inference algorithms for SBL become too expensive for high-dimensional settings, due to the need to…

Vocal Bursts Intensity Prediction