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Abstract.  The modelling process in Bayesian Statistics constitutes the fundamental stage of the analysis, since depending on the chosen probability laws the inferences may vary considerably. This is particularly true when conflicts arise between two or more sources of information. For instance, inference in the presence of an outlier (which conflicts with the information provided by the other observations) can be highly dependent on the assumed sampling distribution. When heavy‐tailed (e.g. t) distributions are used, outliers may be rejected whereas this kind of robust inference is not available when we use light‐tailed (e.g. normal) distributions. A long literature has established sufficient conditions on location‐parameter models to resolve conflict in various ways. In this work, we consider a location–scale parameter structure, which is more complex than the single parameter cases because conflicts can arise between three sources of information, namely the likelihood, the prior distribution for the location parameter and the prior for the scale parameter. We establish sufficient conditions on the distributions in a location–scale model to resolve conflicts in different ways as a single observation tends to infinity. In addition, for each case, we explicitly give the limiting posterior distributions as the conflict becomes more extreme.

In general, meteorologists find it difficult to make seasonal predictions in the north-east region of Brazil due to the contrasting atmospheric phenomena that take place there. The rain prophets claim to be able to predict the seasonal weather by observing the behavior of nature. Their predictions have a strong degree of subjectivity; this makes science (especially meteorology) disregard these predictions, which could be a relevant source of information for prediction models. In this article, we regard the prophets' knowledge from a subjectivist point of view: we apply elicitation of expert knowledge techniques to extract their opinions and convert them into probability densities that represent their predictions of forthcoming rainy seasons.Brazilian climate, elicitation, Kumaraswamy distribution, rain prophets, seasonal weather,

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