Order-Based Dependent Dirichlet Processes

By Jim E. Griffin, Mark F. J. Steel. 2006

403 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/0602045e1c2bf0055a9da42bb8aa0a0bf88e7da4

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

Citations in the corpus

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.”
Regression Density Estimation With Variational Methods and Stochastic Approximation (2012, methodology): “We do not discuss these methods any further, but refer readers to MacEachern (1999), De Iorio et al. (2004), Griffin and Steel (2006), and Dunson, Pillai, and Park (2007) and the references therein for a summary of relevant methodology and recent developments.”
Bayesian dynamic density estimation (2008, methodology): “Particularly relevant for this paper are the works of Griffin and Steel (2006), who induce dependency through random permutations of the atoms; Griffin and Steel (2007), who construct the mixing distribution at a new time point as a linear combination of the mixing distributions at the previous…”
Distance dependent Chinese restaurant processes (2009, neutral): “ly, which is marginally invariant. 4.3 Random-measure models A number of Bayesian models, including the dependent Dirichlet process (MacEachern, 1999) and the order-based dependent Dirichlet process (Grifﬁn and Steel, 2006), induce dependence between the distributions of samples obtained at differen”
POSTERIOR CONSISTENCY IN CONDITIONAL DENSITY ESTIMATION BY COVARIATE DEPENDENT MIXTURES (2011, background): “MacEachern (1999), De Iorio, Muller, Rosner, and MacEachern (2004), Griffin and Steel (2006), Dunson and Park (2008), and Chung and Dunson (2009), among others, developed dependent Dirichlet processes in which conditional distribution is modeled as a mixture with covariate dependent mixing…”
Simultaneous Linear Quantile Regression: A Semiparametric Bayesian Approach (2012, background): “This approach has recently gained attention in the Bayesian literature with the introduction of a number of non-parametric and semi-parametric prior distributions on conditional densities (Müller et al. 1996; MacEachern 1999, 2000; Griffin and Steel 2006; Dunson et al. 2007; Tokdar et al. 2010).”
Stick-breaking autoregressive processes (2011, background): “A related approach is described by Caron et al. (2007) who define a time-dependent nonparametric prior with Dirichlet process marginals by allowing atoms to be deleted (unlike Griffin and Steel (2006)) as well as added at each discrete time point.”
Conditional (2019, neutral): “During the inference process, we render the face from the 3D descriptors ˆ ρ to obtain I 3 d as the input to the U-Net encoder, take the de-identified face I d as the input to the texture encoder and inject the last condition C .”
Smoothly mixing regressions (2007, methodology): “DeIorio et al. (2004) uses this strategy for categorical covariates, and Griffin and Steel (2005) for continuous covariates.”
The matrix stick-breaking process for flexible multi-task learning (2007, methodology): “Rapid convergence has been observed in the diagnostic tests as described in Geweke (1992) and Raftery and Lewis (1992).”
The Dependent Dirichlet Process and Related Models (2020, methodology): “Griffin and Steel (2006) define another interesting variation of the basic DDP by keeping both sets of parameters, locations and the fractions (Vh), unchanged across x.”
A Tutorial on Bayesian Nonparametric Models (2011, positive): “This representation of the Dirichlet process, and its corresponding use in a Dirichlet process mixture, allows us to compute a variety of functions of posterior DPs (Gelfand and Kottas, 2002) and is the basis for the variational approach to approximate inference (Blei and Jordan, 2006).”
The Two-sided Weibull Distribution and Forecasting Financial Tail Risk (2013, background): “The Gaussian, Student t, Skewed t of Hansen (1994), the AL of Chen et al (2011)”
Bayesian Nonparametric Calibration and Combination of Predictive Distributions (2015, neutral): “This type of prior, and its multivariate extensions (e.g., see Müller, Quintana, and Rosner 2004 and Griffin and Steel 2006), is now widely used in many fields due to the availability of efficient algorithms for posterior computations (Escobar and West 1995; MacEachern and Müller 1998;…”
Bayesian density regression (2007, methodology): “Potentially, we could extend model (3) to allow greater flexibility through a predictor-dependent stick breaking process, as described by Griffin and Steel (2006) and Duan et al. (2005).”
Geostatistical inference under preferential sampling (2010, methodology): “These approaches could be extended to the setting that is presented by Diggle and his colleagues by replacing the Gaussian spatial model for S with a non-Gaussian spatial model (e.g. Gelfand et al. (2005), Grifﬁn and Steel (2006) and Reich and Fuentes (2007)).”
Hybrid Dirichlet mixture models for functional data (2009, background): “Recent proposals of multiple-p stick breaking priors (see for example Griffin and Steel (2006), Dunson and Park (2008) and Dunson et al. (2008)) allow for a more general dependence structure across the groups’ random marginal distributions. However, the resulting functional dependence in the data, a”