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Bayesian inference in hierarchical models by combining independent posteriors
[article]
2016
arXiv
pre-print
Hierarchical models are versatile tools for joint modeling of data sets arising from different, but related, sources. Fully Bayesian inference may, however, become computationally prohibitive if the source-specific data models are complex, or if the number of sources is very large. To facilitate computation, we propose an approach, where inference is first made independently for the parameters of each data set, whereupon the obtained posterior samples are used as observed data in a substitute
arXiv:1603.09272v2
fatcat:zpky44sqqjccbn3ig7fx6ftsti