i.e. joint analyses are invariant to permutations of the study indices, then a model which consistently predicts accurately across the available $K$ studies is a good candidate for a generalist use, to predict $Y$ in future studies $k > K$. This class of prediction functions has been studied in the literature [@sutton2008recent; @tseng2012comprehensive; @pasolli2016machine] and several contributions are based on hierarchical models [@warn2002bayesian; @babapulle2004hierarchical; @higgins2009re]. Similarly, when the exchangeability assumption is inadequate, joint models for multiple studies can incorporate information on relevant relations between studies to construct generalist models[@moreno2012unifying]. For example, when $K$ studies are collected at different time points $t_1 < t_2 < . . . < t_K$, the development of a generalist model can incorporate potential cycles or short-term trends.

Specialist predictions are in contrast directed to predicting future outcomes $Y$ based on covariates $X$ in the context of a specific study $k$ in $\{ 1, \ldots K \}$ –for example a geographic area– represented by one of the $K$ datasets. Bayesian models can be used to borrow information and leverage $K-1$ datasets in addition to the targeted study $k$. Typically the degree of heterogeneity of the distributions $(P_1, \ldots , P_K)$ affects the extent of improvement in 