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MCMC inference in (Conditionally) Conjugate Dirichlet Process Gaussian Mixture Models




We compare the predictive accuracy of the Dirichlet Process Gaussian mixture models using conjugate and conditionally conjugate priors and show that better density models result from using the wider class of priors. We explore several MCMC schemes exploiting conditional conjugacy and show their computational merits on several multidimensional density estimation problems.

Author(s): Rasmussen, CE. and Görür, D.
Year: 2006
Month: June
Day: 0

Department(s): Empirical Inference
Bibtex Type: Talk (talk)

Digital: 0
Event Name: ICML Workshop on Learning with Nonparametric Bayesian Methods 2006
Event Place: Pittsburgh, PA, USA
Language: en
Organization: Max-Planck-Gesellschaft
School: Biologische Kybernetik

Links: Web


  title = {MCMC inference in (Conditionally) Conjugate Dirichlet Process Gaussian Mixture Models},
  author = {Rasmussen, CE. and G{\"o}r{\"u}r, D.},
  organization = {Max-Planck-Gesellschaft},
  school = {Biologische Kybernetik},
  month = jun,
  year = {2006},
  month_numeric = {6}