### A joint calibration model for combining predictive distributions

#### Abstract

In this context, we propose the use of a Bayesian approach to information combining, which consists in treating the predictive probability density functions (pdfs) from the individual ensemble members as data in a Bayesian updating problem. The likelihood function is shown to be proportional to the product of the pdfs, adjusted by a joint “calibration function” describing the predicting skill of the sources (Morris, 1977). In this paper, after rephrasing Morris’ algorithm in a predictive context, we propose to model the calibration function in terms of bias, scale and correlation and to estimate its parameters according to the least squares criterion. The performance of our method is investigated and compared with that of Bayesian Model Averaging (Raftery, 2005) on simulated data.

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DOI: 10.6092/issn.1973-2201/3505