Explaining Variational Approximations
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- repec:spr:psycho:v:82:y:2017:i:3:d:10.1007_s11336-017-9555-z is not listed on IDEAS
- 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.
- Ruben Loaiza-Maya & Michael Stanley Smith, 2017. "Variational Bayes Estimation of Time Series Copulas for Multivariate Ordinal and Mixed Data," Papers 1712.09150, arXiv.org.
- 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.
- 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.
- 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.
- Angelo Mele, 2013. "Approximate variational inference for a model of social interactions," Working Papers 13-16, NET Institute.
- Quiroz, Matias & Villani, Mattias & Kohn, Robert, 2015. "Speeding Up Mcmc By Efficient Data Subsampling," Working Paper Series 297, Sveriges Riksbank (Central Bank of Sweden).
- 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.
- 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.
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"The dynamic factor network model with an application to global credit risk,"
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- repec:spr:jagbes:v:22:y:2017:i:3:d:10.1007_s13253-017-0294-5 is not listed on IDEAS
- 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.
- 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.
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- 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.
- Gholamreza Hajargasht, 2015. "Stochastic frontiers with a Rayleigh distribution," Journal of Productivity Analysis, Springer, vol. 44(2), pages 199-208, October.
- Gerlach, Richard & Abeywardana, Sachin, 2016. "Variational Bayes for assessment of dynamic quantile forecasts," International Journal of Forecasting, Elsevier, vol. 32(4), pages 1385-1402.
- Ormerod, John T., 2011. "Grid based variational approximations," Computational Statistics & Data Analysis, Elsevier, vol. 55(1), pages 45-56, January.
- 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.
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