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Marginal likelihood estimation via power posteriors

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Cited by:

  1. Chris J. Oates & Mark Girolami & Nicolas Chopin, 2017. "Control functionals for Monte Carlo integration," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 79(3), pages 695-718, June.
  2. Drovandi, Christopher C. & McGree, James M. & Pettitt, Anthony N., 2013. "Sequential Monte Carlo for Bayesian sequentially designed experiments for discrete data," Computational Statistics & Data Analysis, Elsevier, vol. 57(1), pages 320-335.
  3. Johnston, Reuben & Sarkani, Shahryar & Mazzuchi, Thomas & Holzer, Thomas & Eveleigh, Timothy, 2019. "Bayesian-model averaging using MCMCBayes for web-browser vulnerability discovery," Reliability Engineering and System Safety, Elsevier, vol. 183(C), pages 341-359.
  4. Lefebvre, Geneviève & Steele, Russell & Vandal, Alain C., 2010. "A path sampling identity for computing the Kullback-Leibler and J divergences," Computational Statistics & Data Analysis, Elsevier, vol. 54(7), pages 1719-1731, July.
  5. Joshua C. C. Chan & Eric Eisenstat, 2015. "Marginal Likelihood Estimation with the Cross-Entropy Method," Econometric Reviews, Taylor & Francis Journals, vol. 34(3), pages 256-285, March.
  6. Perrakis, Konstantinos & Ntzoufras, Ioannis & Tsionas, Efthymios G., 2014. "On the use of marginal posteriors in marginal likelihood estimation via importance sampling," Computational Statistics & Data Analysis, Elsevier, vol. 77(C), pages 54-69.
  7. Luigi Spezia & Andy Vinten & Roberta Paroli & Marc Stutter, 2021. "An evolutionary Monte Carlo method for the analysis of turbidity high‐frequency time series through Markov switching autoregressive models," Environmetrics, John Wiley & Sons, Ltd., vol. 32(8), December.
  8. Zhang, Yifan & Fong, Duncan K.H. & DeSarbo, Wayne S., 2021. "A generalized ordinal finite mixture regression model for market segmentation," International Journal of Research in Marketing, Elsevier, vol. 38(4), pages 1055-1072.
  9. Alzahrani, Naif & Neal, Peter & Spencer, Simon E.F. & McKinley, Trevelyan J. & Touloupou, Panayiota, 2018. "Model selection for time series of count data," Computational Statistics & Data Analysis, Elsevier, vol. 122(C), pages 33-44.
  10. Liu, Xiaobin & Li, Yong & Yu, Jun & Zeng, Tao, 2022. "Posterior-based Wald-type statistics for hypothesis testing," Journal of Econometrics, Elsevier, vol. 230(1), pages 83-113.
  11. Milotti, Edoardo & Vyshemirsky, Vladislav & Stella, Sabrina & Dogo, Federico & Chignola, Roberto, 2017. "Analysis of the fluctuations of the tumour/host interface," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 486(C), pages 587-594.
  12. Agbeyegbe, Terence D., 2020. "Bayesian analysis of output gap in Barbados," Latin American Journal of Central Banking (previously Monetaria), Elsevier, vol. 1(1).
  13. Marco Grzegorczyk & Andrej Aderhold & Dirk Husmeier, 2017. "Targeting Bayes factors with direct-path non-equilibrium thermodynamic integration," Computational Statistics, Springer, vol. 32(2), pages 717-761, June.
  14. Xing Ju Lee & Christopher C. Drovandi & Anthony N. Pettitt, 2015. "Model choice problems using approximate Bayesian computation with applications to pathogen transmission data sets," Biometrics, The International Biometric Society, vol. 71(1), pages 198-207, March.
  15. Luca Martino & Fernando Llorente & Ernesto Curbelo & Javier López-Santiago & Joaquín Míguez, 2021. "Automatic Tempered Posterior Distributions for Bayesian Inversion Problems," Mathematics, MDPI, vol. 9(7), pages 1-17, April.
  16. Calderhead, Ben & Girolami, Mark, 2009. "Estimating Bayes factors via thermodynamic integration and population MCMC," Computational Statistics & Data Analysis, Elsevier, vol. 53(12), pages 4028-4045, October.
  17. Jeong Eun Lee & Christian Robert, 2013. "Imortance Sampling Schemes for Evidence Approximation in Mixture Models," Working Papers 2013-42, Center for Research in Economics and Statistics.
  18. Fouskakis, Dimitris & Ntzoufras, Ioannis & Perrakis, Konstantinos, 2020. "Variations of power-expected-posterior priors in normal regression models," Computational Statistics & Data Analysis, Elsevier, vol. 143(C).
  19. Heaps, Sarah E. & Boys, Richard J. & Farrow, Malcolm, 2014. "Computation of marginal likelihoods with data-dependent support for latent variables," Computational Statistics & Data Analysis, Elsevier, vol. 71(C), pages 392-401.
  20. Heaps Sarah E. & Nye Tom M.W. & Boys Richard J. & Williams Tom A. & Embley T. Martin, 2014. "Bayesian modelling of compositional heterogeneity in molecular phylogenetics," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 13(5), pages 1-21, October.
  21. Joshua C. C. Chan & Liana Jacobi & Dan Zhu, 2022. "An automated prior robustness analysis in Bayesian model comparison," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(3), pages 583-602, April.
  22. Chan, Joshua C.C. & Grant, Angelia L., 2016. "Fast computation of the deviance information criterion for latent variable models," Computational Statistics & Data Analysis, Elsevier, vol. 100(C), pages 847-859.
  23. Ryan, Elizabeth G. & Drovandi, Christopher C. & Thompson, M. Helen & Pettitt, Anthony N., 2014. "Towards Bayesian experimental design for nonlinear models that require a large number of sampling times," Computational Statistics & Data Analysis, Elsevier, vol. 70(C), pages 45-60.
  24. Spezia, Luigi, 2020. "Bayesian variable selection in non-homogeneous hidden Markov models through an evolutionary Monte Carlo method," Computational Statistics & Data Analysis, Elsevier, vol. 143(C).
  25. Ioannis Papageorgiou & Ioannis Kontoyiannis, 2023. "The Bayesian Context Trees State Space Model for time series modelling and forecasting," Papers 2308.00913, arXiv.org, revised Oct 2023.
  26. Filippone, Maurizio & Sanguinetti, Guido, 2011. "Approximate inference of the bandwidth in multivariate kernel density estimation," Computational Statistics & Data Analysis, Elsevier, vol. 55(12), pages 3104-3122, December.
  27. Will Penny & Biswa Sengupta, 2016. "Annealed Importance Sampling for Neural Mass Models," PLOS Computational Biology, Public Library of Science, vol. 12(3), pages 1-25, March.
  28. repec:dau:papers:123456789/5724 is not listed on IDEAS
  29. Christopher C. Drovandi & Anthony N. Pettitt, 2013. "Bayesian Experimental Design for Models with Intractable Likelihoods," Biometrics, The International Biometric Society, vol. 69(4), pages 937-948, December.
  30. Li, Yong & Wang, Nianling & Yu, Jun, 2023. "Improved marginal likelihood estimation via power posteriors and importance sampling," Journal of Econometrics, Elsevier, vol. 234(1), pages 28-52.
  31. Mathias Drton & Martyn Plummer, 2017. "A Bayesian information criterion for singular models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 79(2), pages 323-380, March.
  32. Spezia, L. & Cooksley, S.L. & Brewer, M.J. & Donnelly, D. & Tree, A., 2014. "Modelling species abundance in a river by Negative Binomial hidden Markov models," Computational Statistics & Data Analysis, Elsevier, vol. 71(C), pages 599-614.
  33. Johnston, Reuben & Sarkani, Shahryar & Mazzuchi, Thomas & Holzer, Thomas & Eveleigh, Timothy, 2018. "Multivariate models using MCMCBayes for web-browser vulnerability discovery," Reliability Engineering and System Safety, Elsevier, vol. 176(C), pages 52-61.
  34. Ehlers, Ricardo S., 2012. "Computational tools for comparing asymmetric GARCH models via Bayes factors," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 82(5), pages 858-867.
  35. Adam J. Branscum & Dunlei Cheng & J. Jack Lee, 2015. "Testing hypotheses about medical test accuracy: considerations for design and inference," Journal of Applied Statistics, Taylor & Francis Journals, vol. 42(5), pages 1106-1119, May.
  36. Loza-Reyes, E. & Hurn, M.A. & Robinson, A., 2014. "Classification of molecular sequence data using Bayesian phylogenetic mixture models," Computational Statistics & Data Analysis, Elsevier, vol. 75(C), pages 81-95.
  37. AWLP Thilan & P Menéndez & JM McGree, 2023. "Assessing the ability of adaptive designs to capture trends in hard coral cover," Environmetrics, John Wiley & Sons, Ltd., vol. 34(6), September.
  38. Vitoratou, Silia & Ntzoufras, Ioannis & Moustaki, Irini, 2016. "Explaining the behavior of joint and marginal Monte Carlo estimators in latent variable models with independence assumptions," LSE Research Online Documents on Economics 57685, London School of Economics and Political Science, LSE Library.
  39. Elaine A. Ferguson & Jason Matthiopoulos & Robert H. Insall & Dirk Husmeier, 2017. "Statistical inference of the mechanisms driving collective cell movement," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 66(4), pages 869-890, August.
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