@article{oai:nagasaki-u.repo.nii.ac.jp:00010076,
 author = {Shikimi, Takuhisa},
 issue = {4},
 journal = {経営と経済, Journal of Business and Economics},
 month = {Mar},
 note = {Suppose that X1,X2,... are conditionally i.i.d. random variables with
distribution Pθ given  ＝θ，where  is an unknown parameter. If Pθ
is a normal distribution with mean θ and known variance σ2, and if the
prior of  is chosen from the conjugate family N(μ,v2) or proportional
to the Lebesgue measure, then it follows that the posterior distributions
given X1,...,Xn obey a large deviation principle with a rate function. If
Pθ is a normal distribution with known mean and unknown precision θ,
and if as a prior we choose the gamma distribution with parameters α
and β or the improper distribution (1/θ)dθ，the Jeffereys' prior, then
the posterior distributions of  given X1,...,Xn are shown to satisfy a
large deviation principle. The G  artner-Ellis theorem plays the key role
to prove these large deviation principles for the posterior distributions., 經營と經濟, vol.91(4), pp.149-160; 2012},
 pages = {149--160},
 title = {Large Deviation Principles for Posterior Distributions of the Normal Parameters},
 volume = {91},
 year = {2012}
}