Colouring and breaking sticks: random distributions and heterogeneous clustering

  • Green P
  • 43


    Mendeley users who have this article in their library.
  • N/A


    Citations of this article.


We begin by reviewing some probabilistic results about the Dirichlet Process and its close relatives, focussing on their implications for statistical modelling and analysis. We then introduce a class of simple mixture models in which clusters are of different `colours', with statistical characteristics that are constant within colours, but different between colours. Thus cluster identities are exchangeable only within colours. The basic form of our model is a variant on the familiar Dirichlet process, and we find that much of the standard modelling and computational machinery associated with the Dirichlet process may be readily adapted to our generalisation. The methodology is illustrated with an application to the partially-parametric clustering of gene expression profiles.

Author-supplied keywords

  • bayesian nonparametrics
  • gene expression profiles
  • hierarch-
  • ical models
  • loss functions
  • mcmc samplers
  • optimal clustering
  • parti-

Get free article suggestions today

Mendeley saves you time finding and organizing research

Sign up here
Already have an account ?Sign in

Find this document


  • Peter J. Green

Cite this document

Choose a citation style from the tabs below

Save time finding and organizing research with Mendeley

Sign up for free