# Two-Stage Metropolis-Hastings for Tall Data

arXiv: https://arxiv.org/abs/1411.5653 · HTML: https://mlsanity.com/paper/1411.5653/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Nov 2014.

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

- [General bounds on the quality of Bayesian coresets](https://mlsanity.com/paper/2405.11780/) (2024)

## Abstract (arXiv)

This paper discusses the challenges presented by tall data problems associated with Bayesian classification (specifically binary classification) and the existing methods to handle them. Current methods include parallelizing the likelihood, subsampling, and consensus Monte Carlo. A new method based on the two-stage Metropolis-Hastings algorithm is also proposed. The purpose of this algorithm is to reduce the exact likelihood computational cost in the tall data situation. In the first stage, a new proposal is tested by the approximate likelihood based model. The full likelihood based posterior computation will be conducted only if the proposal passes the first stage screening. Furthermore, this method can be adopted into the consensus Monte Carlo framework. The two-stage method is applied to logistic regression, hierarchical logistic regression, and Bayesian multivariate adaptive regression splines.