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Fast inference in generalized linear models via expected log-likelihoods
[article]
2013
arXiv
pre-print
Generalized linear models play an essential role in a wide variety of statistical applications. This paper discusses an approximation of the likelihood in these models that can greatly facilitate computation. The basic idea is to replace a sum that appears in the exact log-likelihood by an expectation over the model covariates; the resulting "expected log-likelihood" can in many cases be computed significantly faster than the exact log-likelihood. In many neuroscience experiments the
arXiv:1305.5712v1
fatcat:qp6df6wbmrarffgxvsm23h6xi4