This chapter aims to elucidate the concepts of
non-informative and informative prior distributions concerning the variance, a
crucial component of the unknown parameter within a normal distribution. The
variance serves as a representation of the distribution's spread or
variability. The estimation variance is exhibited in the point and interval
estimations based on non-informative and informative prior distributions. Point
estimation entails furnishing a specific value to estimate a population
parameter. In contrast, interval estimation provides a range or interval of values
to estimate a population parameter, commonly called a confidence interval.
While non-informative priors express a need for prior knowledge, informative
priors bring valuable insight into the modeling process. The non-informative
priors encompass the maximum likelihood method, the Jackknife method, and the
bootstrap method. Maximum likelihood is widely recognized for approximating
parameters and boasting properties such as unbiased estimation, consistency,
and efficiency. The informative prior distribution employs the Bayesian and
Markov Chain Monte Carlo methods. These methods involve the prior distribution,
given the probability distribution and approach to the posterior distribution.
Author(s) Details:
Autcha Araveeporn,
Department of Statistics, School of Science, King Mongkut’s
Institute of Technology Ladkrabang, Bangkok-10520, Thailand.
Please see the link here: https://stm.bookpi.org/RUMCS-V4/article/view/14101
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