# A Nonparametric Bayesian Approach for Sparse Sequence Estimation

## TL;DR

This paper introduces a nonparametric Bayesian method using Gaussian mixtures for sparse sequence estimation, achieving optimal asymptotic behavior and superior empirical performance with a variational algorithm.

## Contribution

It proposes a novel Gaussian mixture prior for sparse sequence estimation and a deterministic variational algorithm for efficient posterior computation.

## Key findings

- Posterior concentrates at minimax rate
- Gaussian mixtures outperform pure Gaussian priors
- Algorithm shows superior results on benchmark data

## Abstract

A nonparametric Bayes approach is proposed for the problem of estimating a sparse sequence based on Gaussian random variables. We adopt the popular two-group prior with one component being a point mass at zero, and the other component being a mixture of Gaussian distributions. Although the Gaussian family has been shown to be suboptimal for this problem, we find that Gaussian mixtures, with a proper choice on the means and mixing weights, have the desired asymptotic behavior, e.g., the corresponding posterior concentrates on balls with the desired minimax rate. To achieve computation efficiency, we propose to obtain the posterior distribution using a deterministic variational algorithm. Empirical studies on several benchmark data sets demonstrate the superior performance of the proposed algorithm compared to other alternatives.