Variational approximations in Bayesian model selection for finite mixture distributions

@article{McGrory2007VariationalAI,
  title={Variational approximations in Bayesian model selection for finite mixture distributions},
  author={Clare A. McGrory and D. M. Titterington},
  journal={Computational Statistics & Data Analysis},
  year={2007},
  volume={51},
  pages={5352-5367}
}
Variational methods for model comparison have become popular in the neural computing/machine learning literature. In this paper we explore their application to the Bayesian analysis of mixtures of Gaussians. We also consider how the Deviance Information Criterion, or DIC, devised by Spiegelhalter et al. (2002), can be extended to these types of model by exploiting the use of variational approximations. We illustrate the results of using variational methods for model selection and the… CONTINUE READING