  In this article, I investigate the use of Bayesian updating rules applied to
modeling social agents in the case of continuos opinions models. Given another
agent statement about the continuous value of a variable $x$, we will see that
interesting dynamics emerge when an agent assigns a likelihood to that value
that is a mixture of a Gaussian and a Uniform distribution. This represents the
idea the other agent might have no idea about what he is talking about. The
effect of updating only the first moments of the distribution will be studied.
and we will see that this generates results similar to those of the Bounded
Confidence models. By also updating the second moment, several different
opinions always survive in the long run. However, depending on the probability
of error and initial uncertainty, those opinions might be clustered around a
central value.
