Bayesian Deep Net GLM and GLMM

DOI10.1080/10618600.2019.1637747OpenAlexW2963991960WikidataQ127609228 ScholiaQ127609228MaRDI QIDQ3391454

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Publication date: 28 March 2022

Published in: Journal of Computational and Graphical Statistics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1805.10157

zbMATH Keywords

stochastic optimizationvariable selectionfactor modelsvariational approximationdeep learningreparameterization gradient

Mathematics Subject Classification ID

Statistics (62-XX)

Related Items (9)

VBayesLab ⋮ Marginally Calibrated Deep Distributional Regression ⋮ Assessment and Adjustment of Approximate Inference Algorithms Using the Law of Total Variance ⋮ Comparison of deep neural networks and deep hierarchical models for spatio-temporal data ⋮ Horseshoe Regularisation for Machine Learning in Complex and Deep Models¹ ⋮ Marginally calibrated response distributions for end-to-end learning in autonomous driving ⋮ Data augmentation for Bayesian deep learning ⋮ Individualized risk assessment of preoperative opioid use by interpretable neural network regression ⋮ LocalGLMnet: interpretable deep learning for tabular data

Uses Software

• Matlab
• R
• Python
• ADVI
• ADADELTA
• BartPy
• PRMLT

Cites Work

• BART: Bayesian additive regression trees
• Bayesian adaptive Lasso
• Fixed-form variational posterior approximation through stochastic linear regression
• Deep learning: a Bayesian perspective
• Gaussian variational approximation with sparse precision matrices
• Variational Bayes with synthetic likelihood
• Penalized regression, standard errors, and Bayesian Lassos
• Online Model Selection Based on the Variational Bayes
• Latent Variable Models and Factor Analysis
• Gaussian Variational Approximation With a Factor Covariance Structure
• The horseshoe estimator for sparse signals
• Model Selection and Estimation in Regression with Grouped Variables
• Some methods of speeding up the convergence of iteration methods
• Empirical Bayes Gibbs sampling
• A Stochastic Approximation Method
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