Scaling It Up: Stochastic Search Structure Learning in Graphical Models

7 May 2015

Papers citing "Scaling It Up: Stochastic Search Structure Learning in Graphical Models"

10 / 10 papers shown

Title
Network reconstruction via the minimum description length principle Tiago P. Peixoto 23 5 0 02 May 2024
Bayesian Approach to Linear Bayesian Networks Seyong Hwang Kyoungjae Lee Sunmin Oh Gunwoong Park 29 0 0 27 Nov 2023
Inference of multiple high-dimensional networks with the Graphical Horseshoe prior Claudio Busatto F. Stingo 31 2 0 13 Feb 2023
Bayesian Sparse Regression for Mixed Multi-Responses with Application to Runtime Metrics Prediction in Fog Manufacturing Xiaoyu Chen Xiaoning Kang R. Jin Xinwei Deng 11 7 0 10 Oct 2022
Covariance Structure Estimation with Laplace Approximation Bongjung Sung Jaeyong Lee CML 20 1 0 04 Nov 2021
Latent Network Estimation and Variable Selection for Compositional Data via Variational EM Nathan Osborne Christine B. Peterson M. Vannucci BDL 8 18 0 25 Oct 2020
A positive-definiteness-assured block Gibbs sampler for Bayesian graphical models with shrinkage priors Sakae Oya T. Nakatsuma 16 2 0 14 Jan 2020
Bayesian Joint Spike-and-Slab Graphical Lasso Z. Li Tyler H. McCormick S. Clark 19 34 0 18 May 2018
Model-based Clustering with Sparse Covariance Matrices Michael Fop T. B. Murphy Luca Scrucca 32 39 0 21 Nov 2017
An Expectation Conditional Maximization approach for Gaussian graphical models Z. Li Tyler H. McCormick 23 26 0 20 Sep 2017