  Three different inferential problems related to a two dimensional categorical
data from a Bayesian perspective have been discussed in this article. Conjugate
prior distribution with symmetric and asymmetric hyper parameters are
considered. Newly conceived asymmetric prior is based on perceived preferences
of categories. An extension of test of independence by introducing a notion of
measuring association between the parameters has been shown using correlation
matrix. Probabilities of different parametric combinations have been estimated
from the posterior distribution using closed form integration, Monte-Carlo
integration and MCMC methods to draw further inference from categorical data.
Bayesian computation is done using R programming language and illustrated with
appropriate data sets. Study has highlighted the application of Bayesian
inference exploiting the distributional form of underlying parameters.
