On Variational Bayes Estimation and Variational Information Criteria for Linear Regression Models

By Chong You, John Thomas Ormerod, Samuel Müller. 2014

55 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Statistics and Probability

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. David M. Blei
2. Alp Kucukelbir
3. Jon D. McAuliffe
4. I. Castillo
5. A. van der Vaart
6. Johannes Schmidt-Hieber
7. Kolyan Ray
8. Chong You

In Statistics and Probability

1. I. Castillo
2. A. van der Vaart
3. Johannes Schmidt-Hieber
4. Chong You
5. Alp Kucukelbir
6. Judith Rousseau
7. Kolyan Ray
8. Jon D. McAuliffe

Citations in the corpus

• Variational Inference: A Review for Statisticians (2016, background): “usual frequentist asymptotics. (Sometimes the research finds that they do not enjoy the same asymptotics.) Each result revolves around a single model and a single family of variational approximations. You et al. (2014) study the variational posterior for a classical Bayesian linear model. They put a ”
• BAYESIAN LINEAR REGRESSION WITH SPARSE PRIORS (2014, methodology): “A relatively simple and computationally efficient variational Bayes approximation is proposed in [47] and is shown to perform satisfactorily, but examples in the paper are limited to cases where p ≤ n.”
• A variational Bayes approach to variable selection (2017, methodology): “However, as previously shown in You et al. (2014) such criticism does not hold for VB methods in general, at least in an asymptotic sense.”