Variational Bayes for Mixture Models with Censored Data

In this paper, we propose a variational Bayesian algorithm for mixture models that can deal with censored data, which is the data under the situation that the exact value is known only when the value is within a certain range and otherwise only partial information is available. The proposed algorithm can be applied to any mixture model whose component distribution belongs to exponential family; it is a natural generalization of the variational Bayes that deals with "standard" samples whose values are known. We confirm the effectiveness of the proposed algorithm by experiments on synthetic and real world data.