  In Divide & Recombine (D&R), big data are divided into subsets, each analytic
method is applied to subsets, and the outputs are recombined. This enables deep
analysis and practical computational performance. An innovate D\&R procedure is
proposed to compute likelihood functions of data-model (DM) parameters for big
data. The likelihood-model (LM) is a parametric probability density function of
the DM parameters. The density parameters are estimated by fitting the density
to MCMC draws from each subset DM likelihood function, and then the fitted
densities are recombined. The procedure is illustrated using normal and
skew-normal LMs for the logistic regression DM.
