Bayesian Deep Net GLM and GLMM

• Published in: Journal of computational and graphical statistics Vol. 29; no. 1; pp. 97 - 113
• Main Authors: Tran, M.-N., Nguyen, N., Nott, D., Kohn, R.
• Format: Journal Article
• Language: English
• Published: Alexandria Taylor & Francis 02.01.2020; Taylor & Francis Ltd
• Subjects: Approximation; Artificial neural networks; Basis functions; Bayesian analysis; Classification; Computer simulation; Covariance matrix; Deep learning; Factor models; Machine learning; Methods; Neural networks; Optimization; Parameterization; Regression analysis; Reparameterization gradient; Statistical analysis; Statistical inference; Statistical models; Stochastic optimization; Variable selection; Variational approximation

Deep feedforward neural networks (DFNNs) are a powerful tool for functional approximation. We describe flexible versions of generalized linear and generalized linear mixed models incorporating basis functions formed by a DFNN. The consideration of neural networks with random effects is not widely us...