The Validation of Approximate Bayesian Computation: Theory and Practice

  • Martin, Gael (Primary Chief Investigator (PCI))
  • Frazier, David (Chief Investigator (CI))
  • Renault, Eric (Chief Investigator (CI))
  • Robert, Christian (Partner Investigator (PI))

Project: Research

Project Details

Project Description

Given the increased complexity of modern statistical models, current techniques for analyzing those models are being challenged, and new ways of conducting statistical inference being contemplated. Approximate Bayesian computation (ABC) is part of this evolution, beginning to feature in the toolkit of the practicing statistician, and serving as a fresh topic for academic debate and investigation. This project aims to establish the theoretical validity of ABC and to develop rigorous diagnostic methods for assessing its reliability in empirical applications. Advances on this front will have significance in all fields where complex phenomena feature and approximate methods are the only feasible way of understanding those phenomena.
StatusFinished
Effective start/end date1/02/1731/12/21

Funding

  • Australian Research Council (ARC): A$391,000.00
  • Monash University: A$470,522.00
  • Brown University
  • Université Paris Dauphine (Paris Dauphine University)
  • Forecasting: theory and practice

    Petropoulos, F., Apiletti, D., Assimakopoulos, V., Babai, M. Z., Barrow, D. K., Ben Taieb, S., Bergmeir, C., Bessa, R. J., Bijak, J., Boylan, J. E., Browell, J., Carnevale, C., Castle, J. L., Cirillo, P., Clements, M. P., Cordeiro, C., Cyrino Oliveira, F. L., De Baets, S., Dokumentov, A., Ellison, J. & 60 others, Fiszeder, P., Franses, P. H., Frazier, D. T., Gilliland, M., Gönül, M. S., Goodwin, P., Grossi, L., Grushka-Cockayne, Y., Guidolin, M., Guidolin, M., Gunter, U., Guo, X., Guseo, R., Harvey, N., Hendry, D. F., Hollyman, R., Januschowski, T., Jeon, J., Jose, V. R. R., Kang, Y., Koehler, A. B., Kolassa, S., Kourentzes, N., Leva, S., Li, F., Litsiou, K., Makridakis, S., Martin, G. M., Martinez, A. B., Meeran, S., Modis, T., Nikolopoulos, K., Önkal, D., Paccagnini, A., Panagiotelis, A., Panapakidis, I., Pavía, J. M., Pedio, M., Pedregal, D. J., Pinson, P., Ramos, P., Rapach, D. E., Reade, J. J., Rostami-Tabar, B., Rubaszek, M., Sermpinis, G., Shang, H. L., Spiliotis, E., Syntetos, A. A., Talagala, P. D., Talagala, T. S., Tashman, L., Thomakos, D., Thorarinsdottir, T., Todini, E., Trapero Arenas, J. R., Wang, X., Winkler, R. L., Yusupova, A. & Ziel, F., Jul 2022, In: International Journal of Forecasting. 38, 3, p. 705-871 167 p.

    Research output: Contribution to journalReview ArticleResearchpeer-review

    Open Access
    File
    10 Citations (Scopus)
  • Optimal probabilistic forecasts: when do they work?

    Martin, G. M., Loaiza-Maya, R., Maneesoonthorn, W., Frazier, D. T. & Ramírez-Hassan, A., Jan 2022, In: International Journal of Forecasting. 38, 1, p. 384-406 23 p.

    Research output: Contribution to journalArticleResearchpeer-review

  • Focused Bayesian prediction

    Loaiza-Maya, R., Martin, G. M. & Frazier, D. T., Aug 2021, In: Journal of Applied Econometrics. 36, 5, p. 517-543 27 p.

    Research output: Contribution to journalArticleResearchpeer-review

    1 Citation (Scopus)