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Principal Applications of Bayesian Methods in Actuarial Science

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  • Udi Makov

Abstract

Bayesian ideas were introduced into actuarial science in the late 1960s in the form of empirical credibility methods for premium setting. The advance of the Bayesian methodology was slow due to its subjective nature and to the computational difficulties associated with the full Bayesian analysis. This paper offers a brief survey of Bayesian solutions to some actuarial problems and discusses the current state of research.

Suggested Citation

  • Udi Makov, 2001. "Principal Applications of Bayesian Methods in Actuarial Science," North American Actuarial Journal, Taylor & Francis Journals, vol. 5(4), pages 53-57.
    • Handle: RePEc:taf:uaajxx:v:5:y:2001:i:4:p:53-57
    DOI: 10.1080/10920277.2001.10596011
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    Cited by:

    1. Najafabadi, Amir T. Payandeh & Bazaz, Ali Panahi, 2018. "An optimal multi-layer reinsurance policy under conditional tail expectation," Annals of Actuarial Science, Cambridge University Press, vol. 12(1), pages 130-146, March.
    2. Payandeh Najafabadi, Amir T. & Bazaz, Ali Panahi, 2016. "An optimal co-reinsurance strategy," Insurance: Mathematics and Economics, Elsevier, vol. 69(C), pages 149-155.
    3. Migon, Helio S. & Moura, Fernando A.S., 2005. "Hierarchical Bayesian collective risk model: an application to health insurance," Insurance: Mathematics and Economics, Elsevier, vol. 36(2), pages 119-135, April.
    4. Yanwei Zhang & Vanja Dukic, 2013. "Predicting Multivariate Insurance Loss Payments Under the Bayesian Copula Framework," Journal of Risk & Insurance, The American Risk and Insurance Association, vol. 80(4), pages 891-919, December.

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