Variational Bayesian Inference for Parametric and Nonparametric Regression With Missing Data

2011

84 citations recorded by the source corpus.

Citations in the corpus

High-dimensional Bayesian inference via the unadjusted Langevin algorithm (2016, unknown): “e d marginal posterior distributions, where the marginal accuracy between two probability measure µ,ν on (R,B(R)) is deﬁned by MA(µ,ν) = 1−(1/2)kµ −νkTV . This quantity has already been considered in [6] and [9] to compare approximate samplers. To estimate the d marginal posterior distributions, we ”
Penalized Wavelets: Embedding Wavelets into Semiparametric Regression (2011, unknown): “Faes, Ormerod and Wand [22]) and robustness (e.”
Variational Inference for Heteroscedastic Semiparametric Regression (2015, unknown): “This accuracy score is justified in Faes et al. (2011).”
Laplace Variational Approximation for Semiparametric Regression in the Presence of Heteroskedastic Errors (2016, unknown): “…Foundation of North America Journal of Computational and Graphical Statistics, Volume 25, Number 1, Pages 225–245 DOI: 10.1080/10618600.2014.983642 225 mixtures (Attias 2000), linear mixed models (Ormerod and Wand 2010), and nonparametric regression with missing data (Faes, Ormerod, and Wand 2011).”
A variational Bayes approach to variable selection (2017, unknown): “The VB approach is summarized in many places including Bishop (2006); Rue et al. (2009); Ormerod and Wand (2010); Carbonetto and Stephens (2011); Faes et al. (2011); Wand et al. (2011); Murphy (2012); Pham et al. (2013); Luts and Ormerod (2014); Neville et al. (2014); You et al. (2014).”