• Medientyp: Bericht; E-Book
  • Titel: Posterior consistency in conditional density estimation by covariate dependent mixtures
  • Beteiligte: Norets, Andriy [Verfasser:in]; Pelenis, Justinas [Verfasser:in]
  • Erschienen: Vienna: Institute for Advanced Studies (IHS), 2011
  • Sprache: Englisch
  • Schlagwörter: C11 ; Bayesian nonparametrics ; conditional density estimation ; posterior consistency ; C14 ; smoothly mixing regressions ; mixtures of normal distributions ; dependent Dirichlet process ; location-scale mixtures ; mixtures of experts ; kernel stick-breaking process
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  • Beschreibung: This paper considers Bayesian nonparametric estimation of conditional densities by countable mixtures of location-scale densities with covariate dependent mixing probabilities. The mixing probabilities are modeled in two ways. First, we consider finite covariate dependent mixture models, in which the mixing probabilities are proportional to a product of a constant and a kernel and a prior on the number of mixture components is specified. Second, we consider kernel stick-breaking processes for modeling the mixing probabilities. We show that the posterior in these two models is weakly and strongly consistent for a large class of data generating processes.
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