# Average of Recentered Parallel MCMC for Big Data

@article{Wu2017AverageOR, title={Average of Recentered Parallel MCMC for Big Data}, author={Changye Wu and Christian P. Robert}, journal={arXiv: Computation}, year={2017} }

In big data context, traditional MCMC methods, such as Metropolis-Hastings algorithms and hybrid Monte Carlo, scale poorly because of their need to evaluate the likelihood over the whole data set at each iteration. In order to resurrect MCMC methods, numerous approaches belonging to two categories: divide-and-conquer and subsampling, are proposed. In this article, we study the parallel MCMC and propose a new combination method in the divide-and-conquer framework. Compared with some parallel…

## 3 Citations

### Divide and Recombine for Large and Complex Data: Model Likelihood Functions Using MCMC and TRMM Big Data Analysis

- Mathematics
- 2018

An innovate D\&R procedure is proposed to compute likelihood functions of data-model (DM) parameters for big data by fitting the density to MCMC draws from each subset DM likelihood function, and then the fitted densities are recombined.

### Modeling Network Populations via Graph Distances

- Mathematics, Computer ScienceJournal of the American Statistical Association
- 2020

A new class of models for multiple networks to parameterize a distribution on labeled graphs in terms of a Fréchet mean graph and a parameter that controls the concentration of this distribution about its mean is introduced.

### A Survey of Bayesian Statistical Approaches for Big Data

- Computer ScienceCase Studies in Applied Bayesian Data Science
- 2020

The question of whether focusing only on improving computational algorithms and infrastructure will be enough to face the challenges of Big Data is addressed.

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