2021/06/28 by Gunawan, David, Kohn, Robert, Nott, David
#Computation (stat.CO) #FOS: Computer and information sciences #Methodology (stat.ME)
paper · doi:10.48550/arxiv.2106.14392
Variational Bayes methods approximate the posterior density by a family of tractable distributions whose parameters are estimated by optimisation. Variational approximation is useful when exact inference is intractable or very costly. Our article develops a flexible variational approximation based on a copula of a mixture, which is implemented by combining boosting, natural gradient, and a variance reduction method. The efficacy of the approach is illustrated by using simulated and real datasets to approximate multimodal, skewed and heavy-tailed posterior distributions, including an application to Bayesian deep feedforward neural network regression models.