Dynamic and robust Bayesian graphical models

DOI10.1007/s11222-022-10177-0zbMath1499.62023OpenAlexW4308746483MaRDI QIDQ2103986

Daniel R. Kowal, Chunshan Liu, Marina Vannucci

Publication date: 9 December 2022

Published in: Statistics and Computing (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s11222-022-10177-0

zbMATH Keywords

hidden Markov modelpartial correlation\(t\)-distributionhand gesture data

Mathematics Subject Classification ID

Computational methods for problems pertaining to statistics (62-08) Estimation in multivariate analysis (62H12) Applications of statistics to biology and medical sciences; meta analysis (62P10) Bayesian inference (62F15) Markov processes: estimation; hidden Markov models (62M05)

Uses Software

• glasso
• BayesDA

Cites Work

• Unnamed Item
• Sparse inverse covariance estimation with the graphical lasso
• Scaling it up: stochastic search structure learning in graphical models
• Robust graphical modeling of gene networks using classical and alternative \(t\)-distributions
• Robust Bayesian graphical modeling using Dirichlet \(t\)-distributions
• Estimating time-varying networks
• Robust methods for inferring sparse network structures
• Bayesian inference for multiple Gaussian graphical models with application to metabolic association networks
• Loose-limbed people: estimating 3D human pose and motion using non-parametric belief propagation
• Bayesian graphical models for modern biological applications
• Network exploration via the adaptive LASSO and SCAD penalties
• Hierarchical normalized completely random measures for robust graphical modeling
• Robust concentration graph model selection
• Robust Gaussian graphical modeling
• High-dimensional graphs and variable selection with the Lasso
• Markov chain Monte Carlo methods and the label switching problem in Bayesian mixture modeling
• Bayesian graphical Lasso models and efficient posterior computation
• Simultaneous gesture segmentation and recognition based on forward spotting accumulative HMMs
• Estimating Time-Varying Graphical Models
• Hyper Inverse Wishart Distribution for Non-decomposable Graphs and its Application to Bayesian Inference for Gaussian Graphical Models
• Bayesian Methods for Hidden Markov Models
• A Bayesian Approach for Estimating Dynamic Functional Network Connectivity in fMRI Data
• Gibbs Sampling Methods for Stick-Breaking Priors
• Multivariate T-Distributions and Their Applications
• Robust Gaussian Graphical Modeling Via l₁ Penalization
• Latent Network Estimation and Variable Selection for Compositional Data Via Variational EM
• The Joint Graphical Lasso for Inverse Covariance Estimation Across Multiple Classes
• Partial Correlation Estimation by Joint Sparse Regression Models
• Bayesian modeling of multiple structural connectivity networks during the progression of Alzheimer's disease

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