Beta mixture model (BMM) is an important non-Gaussian probability model

which has been widely used in statistical analysis of the bounded data.It is hard to perform parameter estimation for BMM

due to its complex function format.An efficient variational Bayesian learning method has been proposed to deal with this problem.With the variational distribution and by iteratively maximizing the lower bound of the original variational object function

the approximating distribution which is the closest to the true Bayesian posterior distribution is obtained.Both synthetic and real data are experimented to demonstrate the effectiveness and the merits of the proposed approach.