• Organization

  • There are four directories, each correspond to a subsection in the experiments section of the paper: lme (linear mixed effects modeling), mixtures (mixture modeling), ml (MovieLens data), and parafac (probabilistic parafac).
  • Each directory has four sub directories: code, data, qsub, and result.
  • Directory 'code' has files of all the code (Matlab and R source code) that was used in the analysis.
  • Directory 'data' has (if any) simulated data that was used in the analysis. This directory may be empty or absent.
  • Directory 'qsub' has SGE files (.q) that were used to submit jobs on a SGE cluster.
  • Directory 'result' has a sub directory 'img' and stores the result (if any) produced in the analysis. This directory may be empty or absent.

• Files

  • 'simulate_data.R' contains the code to simulate and partition the data.
  • 'analyze_result.R' contains the code for analyzing the results of MCMC, WASP, and competing methods and making plots/tables.
  • 'mcmc_sampler.R' contains the code for the known/standard MCMC/Gibbs sampler for the model.
  • 'wasp_sampler.R' contains the code for the MCMC/Gibbs sampler of a subset posterior distribution. This is a modified version of the code in 'mcmc_sampler.R' using stochastic approximation.
  • 'comp_sampler.R' contains the code for the MCMC/Gibbs sampler of a subset posterior distribution in Consensus Monte Carlo (CMC) or Semiparametric Density Product (SDP). This is a modified version of the code in 'mcmc_sampler.R' by raising the prior to a power of '1/k', where 'k' is the number of subsets.
  • 'variational_bayes.R' contains the code for the variational Bayes approach.
  • 'submit.R' contains the code for the R code for submitting a job on the cluster. The files in 'qsub' directory use this file for running simulations.

• Citation: If you use the code, then please cite the following three papers:

  • Srivastava, S., Li, C. and Dunson, D. B. (2017+). Scalable Bayes via barycenter in Wasserstein space. [https://arxiv.org/abs/1508.05880]
  • Li, C., Srivastava, S. and Dunson, D. B. (2017). Simple, scalable and accurate posterior interval estimation. Biometrika 104: 665-680. [https://arxiv.org/abs/1605.04029]
  • Srivastava, S., Cevher, V., Tran-Dinh, Q. and Dunson, D. B. (2015). WASP: Scalable Bayes via barycenters of subset posteriors. Artificial Intelligence and Statistics: 912-920.

• Contact: Please email Cheng Li ([email protected]) or Sanvesh Srivastava ([email protected]) if you have any questions related to the code.

• Acknowledgment

  • The files 'callLpSolver.m', 'recoverSolution.m', and the *.m files for computing the WASP are based on an algorithm due to Volkan Cevher (https://lions.epfl.ch/) and Quoc Tran-Dinh (http://trandinhquoc.com/). The algorithm can be found in Srivastava et al. (2015).
  • Some code for MovieLens data analysis and linear mixed effects modeling has been borrowed from Patrick O. Perry (http://ptrckprry.com/code/).
  • Please email us if you think that we have missed citations to your paper/work.