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A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types
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Double-Parallel Monte Carlo for Bayesian Analysis of Big Data.

Jingnan Xue1, Faming Liang2

  • 1Department of Statistics, Texas A&M University, College Station, TX 77843.

Statistics and Computing
|April 24, 2019
PubMed
Summary

This study introduces Double Parallel Monte Carlo, an efficient algorithm for analyzing big data using Bayesian methods. It divides large datasets into subsets and uses parallel processing to approximate full data posterior distributions.

Keywords:
Divide-and-CombineEmbarrassingly ParallelMCMCPop-SAMCSubset Posterior Aggregation

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Area of Science:

  • Computational Statistics
  • Big Data Analytics
  • Bayesian Inference

Background:

  • Bayesian analysis of big data presents computational challenges.
  • Existing methods struggle with scalability for large datasets.

Purpose of the Study:

  • To propose an efficient Markov Chain Monte Carlo (MCMC) algorithm for Bayesian analysis of big data.
  • To develop a scalable and practical computational approach for large-scale Bayesian inference.

Main Methods:

  • The proposed algorithm divides big datasets into smaller subsets.
  • Subset posteriors are aggregated to approximate the full data posterior.
  • Population stochastic approximation Monte Carlo (Pop-SAMC) is used for parallel MCMC simulation within subsets.
  • The approach is termed 'Double Parallel Monte Carlo' due to its data and simulation parallel levels.

Main Results:

  • The algorithm provides a simple and efficient method for Bayesian analysis of big data.
  • Mathematical and numerical justifications confirm the algorithm's validity.
  • The 'Double Parallel Monte Carlo' method demonstrates computational efficiency and scalability.

Conclusions:

  • The Double Parallel Monte Carlo algorithm offers a practical solution for big data Bayesian analysis.
  • This novel approach effectively handles large datasets by leveraging parallel computation.
  • The method is mathematically and numerically validated, showing its reliability.