Bayesian methods play a prominent role in parameter estimation and
uncertainty quantification. In a typical application of Bayes theorem, a
prior distribution over the parameters is updated through a likelihood
function to obtain the posterior distribution. In the absence of any
prior knowledge, a non-informative prior is chosen to express lack of
any preference by assigning a uniform distribution over the possible
ranges of parameters. However, the validity of uniform priors as being
truly non-informative is seldom questioned. The objective of this study
is to test this assumption while estimating soil saturated hydraulic
conductivity using data from infiltration experiments. The concept of a
non-informative prior using an information theoretic approach is pursued
for this application, and the results compared to those obtained from
assignment of a uniform prior. Non-informative priors obtained by the
information theoretic approach are different from a uniform prior, and
estimates of the posterior distribution are influenced by the choice of
the prior, especially when data are limited. Examples from both
hypothetical and real data are utilized to highlight the importance of
selecting truly non-informative priors.