Decision Theoretic Aspects of Dependent Nonparametric Processes

By S. MacEachern. 2000

76 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/bcacba5335dc47bd918326b230e8c985305982f2

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Citations in the corpus

Inference for Mixtures of Finite Polya Tree Models (2006, background): “I am also working toward adapting some particular aspects of dependent Dirichlet processes (MacEachern 2001) to Polya trees.”
The Nested Dirichlet Process (2008, methodology): “The Dirichlet process (DP) (Ferguson 1973, 1974) is the most widely used nonparametric model for random distributions in Bayesian statistics, due mainly to the availability of efficient computational techniques (Escobar and West 1995; Lo, Brunner, and Chan 1996; MacEachern and Müller 1998; Neal 2000”
Infinite Latent SVM for Classification and Multi-task Learning (2011, background): “For example, to handle heterogenous observations, predictor-dependent processes [20] have been proposed; and to relax the exchangeability assumption, various correlation structures, such as hierarchical structures [26], temporal or spatial dependencies [5], and stochastic ordering dependencies [13, ”
Bayesian density regression (2007, background): “process (DDP) approach of MacEachern (1999; 2000; 2001) , which relies on expressing the DP in”
Bayesian Nonparametrics: Models beyond the Dirichlet process (2009, unknown): “Based on an initial idea set forth by MacEachern (1999, 2000, 2001) who introduced the so–called dependent Dirichlet process, many subsequent papers have provided variants of the stick–breaking construction so to allow either the random masses p̃j or the random locations Xi to depend on a set of…”
Hybrid Dirichlet mixture models for functional data (2009, methodology): “Then, (a) an hDP.αq, G0/ prior for G is a mixture of DPs ∫ DP.αQ/dμ.”