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Explaining Variational Approximations

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  • Ormerod, J. T.
  • Wand, M. P.

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  • Ormerod, J. T. & Wand, M. P., 2010. "Explaining Variational Approximations," The American Statistician, American Statistical Association, vol. 64(2), pages 140-153.
  • Handle: RePEc:bes:amstat:v:64:i:2:y:2010:p:140-153
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    File URL: http://pubs.amstat.org/doi/abs/10.1198/tast.2010.09058
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    Citations

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    Cited by:

    1. Korobilis, Dimitris & Koop, Gary, 2018. "Variational Bayes inference in high-dimensional time-varying parameter models," Essex Finance Centre Working Papers 22665, University of Essex, Essex Business School.
    2. repec:spr:psycho:v:82:y:2017:i:3:d:10.1007_s11336-017-9555-z is not listed on IDEAS
    3. Arthur White & Thomas Brendan Murphy, 2016. "Exponential family mixed membership models for soft clustering of multivariate data," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 10(4), pages 521-540, December.
    4. Bräuning, Falk & Koopman, Siem Jan, 2016. "The dynamic factor network model with an application to global credit risk," Working Papers 16-13, Federal Reserve Bank of Boston.
    5. Gholamreza Hajargasht, 2015. "Stochastic frontiers with a Rayleigh distribution," Journal of Productivity Analysis, Springer, vol. 44(2), pages 199-208, October.
    6. Elisabeth Waldmann & Thomas Kneib & Yu Ryan Yu & Stefan Lang, 2012. "Bayesian semiparametric additive quantile regression," Working Papers 2012-06, Faculty of Economics and Statistics, University of Innsbruck.
    7. Gerlach, Richard & Abeywardana, Sachin, 2016. "Variational Bayes for assessment of dynamic quantile forecasts," International Journal of Forecasting, Elsevier, vol. 32(4), pages 1385-1402.
    8. Ormerod, John T., 2011. "Grid based variational approximations," Computational Statistics & Data Analysis, Elsevier, vol. 55(1), pages 45-56, January.
    9. Ruben Loaiza-Maya & Michael Stanley Smith, 2017. "Variational Bayes Estimation of Discrete-Margined Copula Models with Application to Time Series," Papers 1712.09150, arXiv.org, revised Jul 2018.
    10. F. S. Nathoo & A. Babul & A. Moiseev & N. Virji-Babul & M. F. Beg, 2014. "A variational Bayes spatiotemporal model for electromagnetic brain mapping," Biometrics, The International Biometric Society, vol. 70(1), pages 132-143, March.
    11. Zhao, Kaifeng & Lian, Heng, 2014. "Variational inferences for partially linear additive models with variable selection," Computational Statistics & Data Analysis, Elsevier, vol. 80(C), pages 223-239.
    12. Elizabeth G. Ryan & Christopher C. Drovandi & James M. McGree & Anthony N. Pettitt, 2016. "A Review of Modern Computational Algorithms for Bayesian Optimal Design," International Statistical Review, International Statistical Institute, vol. 84(1), pages 128-154, April.
    13. Gholamreza Hajargasht & William E. Griffiths, 2016. "Estimation and Testing of Stochastic Frontier Models using Variational Bayes," Department of Economics - Working Papers Series 2024, The University of Melbourne.
    14. Angelo Mele, 2013. "Approximate variational inference for a model of social interactions," Working Papers 13-16, NET Institute.
    15. repec:spr:jagbes:v:22:y:2017:i:3:d:10.1007_s13253-017-0294-5 is not listed on IDEAS
    16. Quiroz, Matias & Villani, Mattias & Kohn, Robert, 2015. "Speeding Up Mcmc By Efficient Data Subsampling," Working Paper Series 297, Sveriges Riksbank (Central Bank of Sweden).
    17. repec:csb:stintr:v:17:y:2016:i:1:p:91-104 is not listed on IDEAS
    18. Nicolas Depraetere & Martina Vandebroek, 2017. "A comparison of variational approximations for fast inference in mixed logit models," Computational Statistics, Springer, vol. 32(1), pages 93-125, March.
    19. McGrory, C.A. & Pettitt, A.N. & Titterington, D.M. & Alston, C.L. & Kelly, M., 2016. "Transdimensional sequential Monte Carlo using variational Bayes — SMCVB," Computational Statistics & Data Analysis, Elsevier, vol. 93(C), pages 246-254.
    20. repec:taf:jnlasa:v:112:y:2017:i:517:p:161-164 is not listed on IDEAS
    21. Nott, David J. & Li, Jialiang & Fielding, Mark, 2011. "Importance sampling as a variational approximation," Statistics & Probability Letters, Elsevier, vol. 81(8), pages 1052-1055, August.
    22. repec:exl:29stat:v:17:y:2016:i:1:p:91-104 is not listed on IDEAS
    23. Luts, Jan & Ormerod, John T., 2014. "Mean field variational Bayesian inference for support vector machine classification," Computational Statistics & Data Analysis, Elsevier, vol. 73(C), pages 163-176.

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