Generalized spatial dirichlet process models

By Jason A. Duan, Michele Guindani, Alan E. Gelfand. 2007

198 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Genetics, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/3fc94d009f8e3694047e759aa3a75f40f9ce5820

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Citations in the corpus

Shared Segmentation of Natural Scenes Using Dependent Pitman-Yor Processes (2008, background): “[24] proposed a generalized spatial Dirichlet processwhich links assignments via thresholded GPs, as in Sec.”
Hierarchical kernel stick-breaking process for multi-task image analysis (2008, methodology): “[8] developed models to allow the weights to change with the predictor.”
Bayesian Nonparametrics: Hierarchical Bayesian nonparametric models with applications (2010, methodology): “To obtain a spatially dependent Pitman-Yor process, Sudderth and Jordan (2009) adapt an idea of Duan et al. (2007), who used a latent collection of Gaussian processes to define a spatially dependent set of draws from a Dirichlet process.”
On the stick-breaking representation of normalized inverse Gaussian priors (2012, background): “Important contributions to this area include, among others, [3, 4, 5, 6, 10, 11, 27].”
Hybrid Dirichlet mixture models for functional data (2009, background): “A less developed discussion of these aspects is provided in Duan et al. (2007).”
Bayesian density regression (2007, methodology): “This model encompasses a wide variety of regression and mixture models as special cases, including normal linear regression, linear regression with the residual density modelled as a finite or infinite mixture of Gaussian distributions and the finite mixture of linear regressions: f.yi|xi/= k∑ h=1…”
The Nested Dirichlet Process (2008, methodology): “This latter class of models was extended by Duan, Guindani, and Gelfand (2007) to create generalized spatial DPs (GSDPs) that allow different surface selection at different locations.”
A Tutorial on Bayesian Nonparametric Models (2011, methodology): “One way to capture such dependencies in a BNP model is to make the base distribution G0 of the DP dependent on a location variable (Gelfand et al., 2005; Duan et al., 2007).”
Distance dependent Chinese restaurant processes (2009, unknown): “Networked data induces an entirely different distance function, where any data point may link to an arbitrary set of other data.”
The Matrix Stick-Breaking Process (2008, background): “We follow recent authors (Griffin and Steel 2006; Duan, Guindani, and Gelfand 2005; Dunson, Pillai, and Park 2007; Dunson and Park 2007) by incorporating dependence through the stickbreaking weights.”