@InProceedings{daume07astar-dp,
  author =       {Hal {Daum\'e III}},
  title =        {Fast search for Dirichlet process mixture models},
  booktitle =    {Proceedings of the Eleventh International Conference on Artificial Intelligence and Statistics (AIStats)},
  year =         {2007},
  address =      {San Juan, Puerto Rico},
  abstract =     {
    Dirichlet process (DP) mixture models provide a flexible Bayesian
    framework for density estimation.  Unfortunately, their flexibility
    comes at a cost: inference in DP mixture models is computationally
    expensive, even when conjugate distributions are used.  In the common
    case when one seeks only a maximum a posteriori assignment of data
    points to clusters, we show that search algorithms provide a practical
    alternative to expensive MCMC and variational techniques.  When a true
    posterior sample is desired, the solution found by search can serve as
    a good initializer for MCMC.  Experimental results show that using
    these techniques is it possible to apply DP mixture models to very
    large data sets.
  },
  url = {http://pub.hal3.name/#daume07astar-dp}
}