The Nested Dirichlet Process

By Abel Rodríguez, David B. Dunson, Alan E. Gelfand. 2008

366 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/d81abf3192002856fc8178922a2a1b5c944d75ef

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Citations in the corpus

Learning with Hierarchical-Deep Models (2013, background): “Index Terms—Deep networks, deep Boltzmann machines, hierarchical Bayesian models, one-shot learning Ç”
The Matrix Stick-Breaking Process (2008, background): “Rodriguez, Dunson, and Gelfand (2008) developed a nested DP, which instead allows clustering of distributions.”
A Sticky HDP-HMM With Application to Speaker Diarization (2009, methodology): “This formulation is related to the nestedDP (Rodriguez et al., 2008), which uses a Dirichlet process to partition data into groups, and then models each group via a Dirichlet process mixture.”
Probabilistic grammars and hierarchical Dirichlet processes (2018, methodology): “Then the HDP-PCFG-GR is equivalent to the nested Dirichlet process (Rodriguez et al., 2008).”
An HDP-HMM for systems with state persistence (2008, background): “This formulation is related to the nested DP (Rodriguez et al., 2006).”
Multi-task learning for sequential data via iHMMs and the nested Dirichlet process (2007, methodology): “, H ) for every j. Rodriguez et al. (2006) showed that the prior cor- L tions {G0 1, . . . , G0 } for the iHMMs are drawn from an J nDP.”
Bayesian dynamic density estimation (2008, background): “…processes (Müller et al. 2004; Dunson et al. 2007b; Griffin and Steel 2007; Dunson 2006) or by introducing dependence in the elements of the stick-breaking representation of the distribution (MacEachern 1999; DeIorio et al. 2004; Gelfand et al. 2005; Griffin and Steel 2006; Rodriguez et al. 2008).”
Bayesian semiparametric modeling of realized covariance matrices (2016, background): “8A related but different approach to allow dependence through a hierarchical structure is the nested DP of Rodriguez et al. (2008). with a base measure that itself is a draw from a DP, can be used as a prior for the rows of the transition matrix of an infinite Markov chain.”
Learning to Learn with Compound HD Models (2011, background): “This ability is the hallmark of hierarchical Bayesian (HB) models, recently proposed in computer vision, statistics, and cognitive science [7, 25, 4, 13] for learning to learn from few examples.”
On posterior contraction of parameters and interpretability in Bayesian mixture modeling (2019, methodology): “Dirichlet process (DP) and its variants have also been adopted as a building block for more sophisticated hierarchical modeling, thanks to the ease with which computational procedures for posterior inference via Markov Chain Monte Carlo can be implemented [50, 42].”