Brazilian Journal of Probability and Statistics

Rejoinder

Steven L. Scott

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Article information

Source
Braz. J. Probab. Stat., Volume 31, Number 4 (2017), 697-700.

Dates
Received: June 2017
Accepted: June 2017
First available in Project Euclid: 15 December 2017

Permanent link to this document
https://projecteuclid.org/euclid.bjps/1513328763

Digital Object Identifier
doi:10.1214/17-BJPS368

Mathematical Reviews number (MathSciNet)
MR3738174

Zentralblatt MATH identifier
1385.65009

Citation

Scott, Steven L. Rejoinder. Braz. J. Probab. Stat. 31 (2017), no. 4, 697--700. doi:10.1214/17-BJPS368. https://projecteuclid.org/euclid.bjps/1513328763


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References

  • Bumbuca, F., Misra, S. and Rossi, P. E. (2017). Distributed Markov chain Monte Carlo for Bayesian hierarchical models. Technical report, available at https://ssrn.com/abstract=2964646.
  • Huang, Z. and Gelman, A. (2005). Sampling for Bayesian computation with large datasets. Technical report, Columbia University Department of Statistics.
  • Scott, S. L., Blocker, A. W., Bonassi, F. V., Chipman, H. A., George, E. I. and McCulloch, R. E. (2016). Bayes and big data: The consensus Monte Carlo algorithm. International Journal of Management Science and Engineering Management 11, 78–88.
  • Wikipedia (2017). https://en.wikipedia.org/wiki/Tensor_processing_unit.
  • Zhang, Y., Duchi, J. C. and Wainwright, M. J. (2012). Communication-efficient algorithms for statistical optimization. In Decision and Control (CDC), 2012 IEEE 51st Annual Conference on, 6792.

See also

  • Main article: Comparing consensus Monte Carlo strategies for distributed Bayesian computation.