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A general framework for updating belief distributions

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Cited by:

  1. Malmendier, Ulrike & Pouzo, Demian & Vanasco, Victoria, 2020. "Investor experiences and international capital flows," Journal of International Economics, Elsevier, vol. 124(C).
  2. Chaya Weerasinghe & Ruben Loaiza-Maya & Gael M. Martin & David T. Frazier, 2023. "ABC-based Forecasting in State Space Models," Monash Econometrics and Business Statistics Working Papers 12/23, Monash University, Department of Econometrics and Business Statistics.
  3. David T. Frazier & Ruben Loaiza-Maya & Gael M. Martin, 2021. "Variational Bayes in State Space Models: Inferential and Predictive Accuracy," Papers 2106.12262, arXiv.org, revised Feb 2022.
  4. Ruben Loaiza‐Maya & Gael M. Martin & David T. Frazier, 2021. "Focused Bayesian prediction," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 36(5), pages 517-543, August.
  5. Fabio Canova & Christian Matthes, 2021. "A Composite Likelihood Approach for Dynamic Structural Models," The Economic Journal, Royal Economic Society, vol. 131(638), pages 2447-2477.
  6. Takuo Matsubara & Jeremias Knoblauch & François‐Xavier Briol & Chris J. Oates, 2022. "Robust generalised Bayesian inference for intractable likelihoods," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 84(3), pages 997-1022, July.
  7. Zhe Wang & Ryan Martin, 2021. "Gibbs posterior inference on a Levy density under discrete sampling," Papers 2109.06567, arXiv.org.
  8. Martin, Gael M. & Frazier, David T. & Maneesoonthorn, Worapree & Loaiza-Maya, Rubén & Huber, Florian & Koop, Gary & Maheu, John & Nibbering, Didier & Panagiotelis, Anastasios, 2024. "Bayesian forecasting in economics and finance: A modern review," International Journal of Forecasting, Elsevier, vol. 40(2), pages 811-839.
  9. Gael M. Martin & David T. Frazier & Ruben Loaiza-Maya & Florian Huber & Gary Koop & John Maheu & Didier Nibbering & Anastasios Panagiotelis, 2023. "Bayesian Forecasting in the 21st Century: A Modern Review," Monash Econometrics and Business Statistics Working Papers 1/23, Monash University, Department of Econometrics and Business Statistics.
  10. Petropoulos, Fotios & Spiliotis, Evangelos & Panagiotelis, Anastasios, 2023. "Model combinations through revised base rates," International Journal of Forecasting, Elsevier, vol. 39(3), pages 1477-1492.
  11. Tsionas, Mike G., 2023. "Joint production in stochastic non-parametric envelopment of data with firm-specific directions," European Journal of Operational Research, Elsevier, vol. 307(3), pages 1336-1347.
  12. Fabio Canova & Christian Matthes, 2021. "Dealing with misspecification in structural macroeconometric models," Quantitative Economics, Econometric Society, vol. 12(2), pages 313-350, May.
  13. Simon N. Wood, 2020. "Inference and computation with generalized additive models and their extensions," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 29(2), pages 307-339, June.
  14. Noel Cressie, 2023. "Decisions, decisions, decisions in an uncertain environment," Environmetrics, John Wiley & Sons, Ltd., vol. 34(1), February.
  15. Romain Fournier & Zoi Tsangalidou & David Reich & Pier Francesco Palamara, 2023. "Haplotype-based inference of recent effective population size in modern and ancient DNA samples," Nature Communications, Nature, vol. 14(1), pages 1-13, December.
  16. Gagnon, Philippe & Wang, Yuxi, 2024. "Robust heavy-tailed versions of generalized linear models with applications in actuarial science," Computational Statistics & Data Analysis, Elsevier, vol. 194(C).
  17. repec:bny:wpaper:0068 is not listed on IDEAS
  18. Tomasz Strzalecki, 2024. "Variational Bayes and non-Bayesian Updating," Papers 2405.08796, arXiv.org, revised May 2024.
  19. Toru Kitagawa & Hugo Lopez & Jeff Rowley, 2022. "Stochastic Treatment Choice with Empirical Welfare Updating," Papers 2211.01537, arXiv.org, revised Feb 2023.
  20. Zhichao Liu & Catherine Forbes & Heather Anderson, 2017. "Robust Bayesian exponentially tilted empirical likelihood method," Monash Econometrics and Business Statistics Working Papers 21/17, Monash University, Department of Econometrics and Business Statistics.
  21. Martin, Ryan & Ouyang, Cheng & Domagni, Francois, 2018. "‘Purposely misspecified’ posterior inference on the volatility of a jump diffusion process," Statistics & Probability Letters, Elsevier, vol. 134(C), pages 106-113.
  22. Smith, Simon C. & Timmermann, Allan & Zhu, Yinchu, 2019. "Variable selection in panel models with breaks," Journal of Econometrics, Elsevier, vol. 212(1), pages 323-344.
  23. Philippas, Dionisis & Dragomirescu-Gaina, Catalin & Goutte, Stéphane & Nguyen, Duc Khuong, 2021. "Investors’ attention and information losses under market stress," Journal of Economic Behavior & Organization, Elsevier, vol. 191(C), pages 1112-1127.
  24. Rangika Peiris & Minh-Ngoc Tran & Chao Wang & Richard Gerlach, 2024. "Loss-based Bayesian Sequential Prediction of Value at Risk with a Long-Memory and Non-linear Realized Volatility Model," Papers 2408.13588, arXiv.org.
  25. The Tien Mai, 2023. "An efficient adaptive MCMC algorithm for Pseudo-Bayesian quantum tomography," Computational Statistics, Springer, vol. 38(2), pages 827-843, June.
  26. Dragomirescu-Gaina, Catalin & Philippas, Dionisis & Tsionas, Mike G., 2021. "Trading off accuracy for speed: Hedge funds' decision-making under uncertainty," International Review of Financial Analysis, Elsevier, vol. 75(C).
  27. Bissiri, Pier Giovanni & Walker, Stephen G., 2019. "On general Bayesian inference using loss functions," Statistics & Probability Letters, Elsevier, vol. 152(C), pages 89-91.
  28. Gael M. Martin & David T. Frazier & Christian P. Robert, 2020. "Computing Bayes: Bayesian Computation from 1763 to the 21st Century," Monash Econometrics and Business Statistics Working Papers 14/20, Monash University, Department of Econometrics and Business Statistics.
  29. Rong Tang & Yun Yang, 2022. "Bayesian inference for risk minimization via exponentially tilted empirical likelihood," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 84(4), pages 1257-1286, September.
  30. Aparajithan Venkateswaran & Anirudh Sankar & Arun G. Chandrasekhar & Tyler H. McCormick, 2024. "Robustly estimating heterogeneity in factorial data using Rashomon Partitions," Papers 2404.02141, arXiv.org, revised Aug 2024.
  31. Gael M. Martin & David T. Frazier & Christian P. Robert, 2021. "Approximating Bayes in the 21st Century," Monash Econometrics and Business Statistics Working Papers 24/21, Monash University, Department of Econometrics and Business Statistics.
  32. Arnau Quera-Bofarull & Joel Dyer & Anisoara Calinescu & Michael Wooldridge, 2023. "Some challenges of calibrating differentiable agent-based models," Papers 2307.01085, arXiv.org.
  33. Nicole H. Lewis & David B. Hitchcock & Ian L. Dryden & John R. Rose, 2018. "Peptide refinement by using a stochastic search," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 67(5), pages 1207-1236, November.
  34. Jack Jewson & David Rossell, 2022. "General Bayesian loss function selection and the use of improper models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 84(5), pages 1640-1665, November.
  35. Andrew Gelman & Christian Hennig, 2017. "Beyond subjective and objective in statistics," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 180(4), pages 967-1033, October.
  36. Yahia Abdel-Aty & Mohamed Kayid & Ghadah Alomani, 2023. "Generalized Bayes Estimation Based on a Joint Type-II Censored Sample from K-Exponential Populations," Mathematics, MDPI, vol. 11(9), pages 1-11, May.
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