Density estimation by dual ascent of the log-likelihood

Research output: Contribution to journalArticlepeer-review


A methodology is developed to assign, from an observed sample, a joint-probability distribution to a set of continuous variables. The algorithm proposed performs this assignment by mapping the original variables onto a jointly-Gaussian set. The map is built iteratively, ascending the log-likelihood of the observations, through a series of steps that move the marginal distributions along a random set of orthogonal directions towards normality.

Original languageEnglish (US)
Pages (from-to)217-233
Number of pages17
JournalCommunications in Mathematical Sciences
Issue number1
StatePublished - 2010


  • Density estimation
  • Machine learning
  • Maximum likelihood

ASJC Scopus subject areas

  • General Mathematics
  • Applied Mathematics


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