Journal article

Approximate Bayesian forecasting

David T Frazier, Worapree Maneesoonthorn, Gael M Martin, Brendan PM McCabe

International Journal of Forecasting | Elsevier | Published : 2019


Approximate Bayesian Computation (ABC) has become increasingly prominent as a method for conducting parameter inference in a range of challenging statistical problems, most notably those characterized by an intractable likelihood function. In this paper, we focus on the use of ABC not as a tool for parametric inference, but as a means of generating probabilistic forecasts; or for conducting what we refer to as ‘approximate Bayesian forecasting’. The four key issues explored are: (i) the link between the theoretical behavior of the ABC posterior and that of the ABC-based predictive; (ii) the use of proper scoring rules to measure the (potential) loss of forecast accuracy when using an approxi..

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University of Melbourne Researchers


Awarded by Australian Research Council

Funding Acknowledgements

We thank the Editor and two anonymous referees for very constructive and detailed comments on earlier drafts of the paper. This research has been supported by Australian Research Council Discovery Grants No. DP150101728 and DP170100729.