Computational Statistics and Data Analysis

By Tung H. Pham, John Thomas Ormerod, Matt P. Wand

28 citations recorded by the source corpus.

A fast mean field variational Bayes (MFVB) approach to nonparametric regression when the predictors are subject to classical measurement error is investigated. It is shown that the use of such technology to the measurement error setting achieves reasonable accuracy. In tandem with the methodological development, a customized Markov chain Monte Carlo method is developed to facilitate the evaluation of accuracy of the MFVB method.

Subfields: Artificial Intelligence, Statistics and Probability

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Citations in the corpus

Variational Inference: A Review for Statisticians (2016, background): “, 2015; Menictas and Wand, 2015), and nonparametric regression models (Pham et al., 2013)”
Bayesian analysis of measurement error models using integrated nested Laplace approximations (2015, methodology): “An alternative approach towards approximate inference in ME models is the variational Bayes method (Bishop, 2006; Ormerod and Wand, 2010), which has been recently applied by Pham et al. (2013) to the special case of simple (parametric and non-parametric) linear regression with classical ME.”
A variational Bayes approach to variable selection (2017, background): “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).”
Semiparametric Mean Field Variational Bayes: General Principles and Numerical Issues (2016, methodology): “In Section 3 we study strategies for solving such problems and apply them to this example in Section 4.2.”