A Variational Inference method for Bayesian variable selection

11/21/2022
by   Lin Guoqiang, et al.
0

Variable selection is a classic problem in statistics. In this paper, we consider a Bayes variable selection problem based on spike-and-slab prior with mixed normal distribution proposed by Ročková and George (2014). Motivated by Ormerod and You(2017, 2022), we use the variational inference and collapsed variational inference method to solve the Bayesian problem instead of MCMC. Like Ormerod and You(2017, 2022), we also explain how the sparsity estimator is induced, and under certain mild assumptions, we also prove the consistent and asymptotic results.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
09/19/2023

Group Spike and Slab Variational Bayes

In this manuscript we introduce Group Spike-and-slab Variational Bayes (...
research
07/11/2022

Sparse Dynamic Factor Models with Loading Selection by Variational Inference

In this paper we develop a novel approach for estimating large and spars...
research
10/25/2020

Latent Network Estimation and Variable Selection for Compositional Data via Variational EM

Network estimation and variable selection have been extensively studied ...
research
06/10/2022

Empirical Likelihood Based Bayesian Variable Selection

Empirical likelihood is a popular nonparametric statistical tool that do...
research
11/08/2021

Fast and Scalable Spike and Slab Variable Selection in High-Dimensional Gaussian Processes

Variable selection in Gaussian processes (GPs) is typically undertaken b...
research
03/06/2019

Economic variable selection

Regression plays a key role in many research areas and its variable sele...
research
05/04/2020

Parameters Estimation from the 21 cm signal using Variational Inference

Upcoming experiments such as Hydrogen Epoch of Reionization Array (HERA)...

Please sign up or login with your details

Forgot password? Click here to reset