Hierarchical priors and mixture models, with applications in regression and density estimation

By Matthew West, Peter Müller, M. Escobar, Peter M Uller. 2006

296 citations recorded by the source corpus.

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Citations in the corpus

Kernel Bayes' Rule (2010, methodology): “In this case, sampling methods are often applied ([21, 22, 41] among others).”
Bayesian inference with dependent normalized completely random measures (2014, methodology): “To circumvent this problem, we resort to the method suggested in [38] and [25]: it consists in resampling, at”
High-Dimensional Sparse Factor Modeling: Applications in Gene Expression Genomics (2008, methodology): “With λ i ∼ F independent and F ∼ Dir (α 0 F 0 ), this is special case of the regression and hierarchical model framework of Dirichlet mixtures as used by West et al. (1994) and MacEachern and Müller (1998).”
Kernel Bayes' rule: Bayesian inference with positive definite kernels (2013, methodology): “In this case, sampling methods are often applied (MacEachern, 1994; West et al., 1994; MacEachern et al., 1999, among others).”
Fast Bayesian Inference in Dirichlet Process Mixture Models (2011, background): “tation, proposing marginal Gibbs sampling ( West et al. 1994; MacEachern 1994; Bush and”
A method for combining inference across related nonparametric Bayesian models (2004, methodology): “West et al. (1994) considered normal hierarchical models with DP mixture priors for density estimation.”
Infinite Mixture Prototypes for Few-Shot Learning (2019, methodology): “When reintroducing variance into multi-modal clustering as we do, a natural approach would be to reconsider the Gibbs sampler for the CRP (West et al., 1994; Neal, 2000) from which DP-means was derived, or other Dirichlet process inference methods such as expectation maximization (Kimura Figure 3.”
Multi-task compressive sensing with Dirichlet process priors (2008, methodology): “In this paper we introduce a Dirichlet process (DP) prior (West et al., 1994) to the hierarchical BCS model, which can simultaneously perform the inversion of the underlying signals and infer the appropriate sharing/clustering structure across the M tasks.”
Semiparametric Bayesian Inference of Long‐Memory Stochastic Volatility Models (2004, methodology): “We choose instead to use the more efficient algorithm of MacEachern (1992) and West et al. (1994), which produces draws that are equivalent to sampling from J|W(z) but by sampling from t,s,K|W(z), where t ¼ (t1,…,tK) is a 2 · K matrix consisting of the distinct elements of J and K £ T ) 1.”
Multi-Task Learning for Classification with Dirichlet Process Priors (2007, methodology): “To make the algorithm more robust, it is suggested by West et al. (1994) and Blei and Jordan (2005) thatα be integrated over a diffuse hyper-prior.”