  A non trivial problem that arises in several applications is the estimation
of the mean of a truncated normal distribution. In this paper, an iterative
deterministic scheme for approximating this mean is proposed. It has been
inspired from an iterative Markov chain Monte Carlo (MCMC) scheme that
addresses this problem and it can be viewed as a generalization of a recently
proposed relevant model. Conditions are provided under which it is proved that
the scheme converges to a unique fixed point. Finally, the theoretical results
are also supported by computer simulations, which also show the rapid
convergence of the method to a solution vector that is very close to the mean
of the truncated normal distribution under study.
