• Corpus ID: 88516387

Nonparametric graphical model for counts

@article{Roy2020NonparametricGM,
  title={Nonparametric graphical model for counts},
  author={Arkaprava Roy and David B. Dunson},
  journal={Journal of machine learning research : JMLR},
  year={2020},
  volume={21}
}
  • Arkaprava Roy, D. Dunson
  • Published 3 January 2019
  • Mathematics, Medicine
  • Journal of machine learning research : JMLR
Although multivariate count data are routinely collected in many application areas, there is surprisingly little work developing flexible models for characterizing their dependence structure. This is particularly true when interest focuses on inferring the conditional independence graph. In this article, we propose a new class of pairwise Markov random field-type models for the joint distribution of a multivariate count vector. By employing a novel type of transformation, we avoid restricting… 
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References

SHOWING 1-10 OF 66 REFERENCES
Bayesian Inference for General Gaussian Graphical Models With Application to Multivariate Lattice Data
TLDR
This work introduces efficient Markov chain Monte Carlo methods for inference and model determination in multivariate and matrix-variate Gaussian graphical models and extends their sampling algorithms to a novel class of conditionally autoregressive models for sparse estimation inMultivariate lattice data.
Bayesian Structure Learning in Sparse Gaussian Graphical Models
TLDR
A novel and efficient Bayesian framework for Gaussian graphical model determination which is a trans-dimensional Markov Chain Monte Carlo (MCMC) approach based on a continuous-time birth-death process and gives a principled and, in practice, sensible approach for structure learning.
Poisson Dependency Networks: Gradient Boosted Models for Multivariate Count Data
TLDR
A novel family of Poisson graphical models, called Poisson Dependency Networks (PDNs), which can model positive and negative dependencies and scale well while often outperforming state-of-the-art, in particular when using multiplicative updates.
On Poisson Graphical Models
TLDR
Three novel approaches provide classes of Poisson-like graphical models that can capture both positive and negative conditional dependencies between count-valued variables, and one can learn the graph structure of these models via penalized neighborhood selection.
Variational Inference for sparse network reconstruction from count data
TLDR
This work adopts a latent model where it directly model counts by means of Poisson distributions that are conditional to latent (hidden) Gaussian correlated variables, and shows that this approach is highly competitive with the existing methods on simulation inspired from microbiological data.
Factor Models for Multivariate Count Data
We develop a general class of factor-analytic models for the analysis of multivariate (truncated) count data. Dependencies in multivariate counts are of interest in many applications, but few
Scaling It Up: Stochastic Search Structure Learning in Graphical Models
Gaussian concentration graph models and covariance graph models are two classes of graphical models that are useful for uncovering latent dependence structures among multivariate variables. In the
Monte Carlo EM Estimation for Time Series Models Involving Counts
Abstract The observations in parameter-driven models for time series of counts are generated from latent unobservable processes that characterize the correlation structure. These models result in
Bayesian Gaussian Copula Factor Models for Mixed Data
TLDR
A novel class of Bayesian Gaussian copula factor models that decouple the latent factors from the marginal distributions is proposed and new theoretical and empirical justifications for using this likelihood in Bayesian inference are provided.
Proper multivariate conditional autoregressive models for spatial data analysis.
TLDR
This work introduces spatial autoregression parameters for multivariate conditional autoregressive models and proposes to employ these models as specifications for second-stage spatial effects in hierarchical models.
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