Statistical models built on the normal distribution are as common in Bayesian statistics

as they are in non-Bayesian approaches. There are several reasons for this. First, nature

seems to have an affinity for this form as evidenced through empirical observation as well as

from the central limit theorem. The weakest form of the central limit theorem essentially

says that an interval measured statistic with bounded variance will eventually be normally

distributed provided sufficient sample size. Therefore it is quite common to see situations

where quantities behave approximately normally. Second, a huge class of posterior distri-

butions can be modeled by combining an assumed normal likelihood function with differing

priors. Finally, when Bayesian models were more difficult to estimate numerically, the nor-

mal distribution sometimes provided an analytically tractable posterior when other forms

were less compliant.