# Bayesian generalized fused lasso modeling via NEG distribution

arXiv: https://arxiv.org/abs/1602.04910 · HTML: https://mlsanity.com/paper/1602.04910/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Feb 2016.

## Built on by (most cited first, 1 of 1)

- [T-LoHo: A Bayesian Regularization Model for Structured Sparsity and Smoothness on Graphs](https://mlsanity.com/paper/2107.02510/) (2021)

## Abstract (arXiv)

The fused lasso penalizes a loss function by the L1 norm for both the regression coefficients and their successive differences to encourage sparsity of both. In this paper, we propose a Bayesian generalized fused lasso modeling based on a normal-exponential-gamma (NEG) prior distribution. The NEG prior is assumed into the difference of successive regression coefficients. The proposed method enables us to construct a more versatile sparse model than the ordinary fused lasso by using a flexible regularization term. We also propose a sparse fused algorithm to produce exact sparse solutions. Simulation studies and real data analyses show that the proposed method has superior performance to the ordinary fused lasso.