The need for combining different sources of information in a probabilistic framework is a
frequent task in earth sciences. This is a need that can be seen when modeling a reservoir …

The general problem of data integration is expressed as that of combining probability
distributions conditioned to each individual datum or data event into a posterior probability …

This paper begins by presenting a simple model of the way in which experts estimate
probabilities. The model is then used to construct a likelihood-based aggregation formula for …

Sequential aggregation is an ensemble forecasting approach that weights each ensemble
member based on past observations and past forecasts. This approach has several …

A random process Z (.) defined on point support has quite different characteristics to those of
aggregations of Z (.). For example, Z (§ ) has larger variance than Z (B)== fBZ (lJ) dlJ/fBdlJ …

Linear pooling is by far the most popular method for combining probability forecasts.
However, any non-trivial weighted average of two or more distinct, calibrated probability …

The resolution of a mixture of two or more populations into its component distributions may
be markedly influenced by one or a few atypical values. The maximum likelihood solution …

Methods to aggregate convex sets of probabilities are proposed. Source reliability is taken
into account by transforming the given information and making it less precise. An important …

Many decision-making processes in the Earth sciences require the combination of multiple
data originating from diverse sources. These data are often indirect and uncertain, and their …

In numerical weather prediction (NWP), the uncertainty about the future state of the
atmosphere is described by a set of forecasts (called an ensemble). All ensembles have …