Adjusted regularization in latent graphical models: application to multiple-neuron spike count data
type: Item
instance of: scholarly article
title: Adjusted regularization in latent graphical models: application to multiple-neuron spike count data
zbMATH Open document ID: 1405.62210
DOI: 10.1214/18-AOAS1190
author: Giuseppe Vinci
author: Valérie Ventura
author: Matthew A. Smith
author: Robert E. Kass
published in: The Annals of Applied Statistics
publication date: 2018-11-16T00:00:00Z
full work available at URL: https://projecteuclid.org/euclid.aoas/1532743486
Mathematics Subject Classification ID: 62P10
Mathematics Subject Classification ID: 62J07
Mathematics Subject Classification ID: 62F15
zbMATH DE Number: 6980485
zbMATH Keywords: Bayesian inference
zbMATH Keywords: Gaussian graphical models
zbMATH Keywords: Gaussian scale mixture
zbMATH Keywords: high dimensionality
zbMATH Keywords: Lasso
zbMATH Keywords: latent variable models
zbMATH Keywords: Macaque prefrontal cortex
zbMATH Keywords: Macaque visual cortex
zbMATH Keywords: Poisson-lognormal
zbMATH Keywords: sparsity
zbMATH Keywords: spike-counts
describes a project that uses: glasso
describes a project that uses: BayesLogit
MaRDI profile type: Publication
Wikidata QID: Q91476825
cites work: Q4768455
cites work: Bayesian structure learning in graphical models
cites work: Q4322338
cites work: The control of the false discovery rate in multiple testing under dependency.
cites work: Maximum likelihood analysis of spike trains of interacting nerve cells
cites work: Latent variable graphical model selection via convex optimization
cites work: Empirical Bayes Analysis of a Microarray Experiment
cites work: Network exploration via the adaptive LASSO and SCAD penalties
cites work: A Biometrics Invited Paper. Stochastic Models for Single Neuron Firing Trains: A Survey
cites work: Sparse inverse covariance estimation with the graphical lasso
cites work: Q4344410
cites work: Discussion: Latent variable graphical model selection via convex optimization
cites work: Ridge Regression: Biased Estimation for Nonorthogonal Problems
cites work: Analysis of neural data
cites work: The graphical lasso: new insights and alternatives
cites work: Bayesian Inference for Logistic Models Using Pólya–Gamma Latent Variables
cites work: High-dimensional covariance estimation by minimizing \(\ell _{1}\)-penalized log-determinant divergence
cites work: Sparse permutation invariant covariance estimation
cites work: Q4864293
cites work: Separating Spike Count Correlation from Firing Rate Correlation
cites work: Adjusted regularization of cortical covariance
cites work: Bayesian graphical Lasso models and efficient posterior computation
cites work: Scaling it up: stochastic search structure learning in graphical models
cites work: Model selection and estimation in the Gaussian graphical model
cites work: Regularization and Variable Selection Via the Elastic Net
OpenAlex ID: W2884403716
Recommended article: Estimating differential latent variable graphical models with applications to brain connectivity
Recommended article: Bayesian Regression With Undirected Network Predictors With an Application to Brain Connectome Data
Recommended article: Scalable Bayesian matrix normal graphical models for brain functional networks
Recommended article: Q5134850
Recommended article: Bayesian modelling and analysis of spatio-temporal neuronal networks
Recommended article: Sparse graphical models via calibrated concave convex procedure with application to fMRI data
Recommended article: Conditional Mixture Model for Correlated Neuronal Spikes
Recommended article: Robust and scalable Bayesian analysis of spatial neural tuning function data
description: scientific article; zbMATH DE number 6980485