Estimating normal means with a conjugate style dirichlet process prior

By S. MacEachern. 1994

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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).”
A Neural Dirichlet Process Mixture Model for Task-Free Continual Learning (2020, methodology): “Since the exact inference of the posterior of DPM models is infeasible, approximate inference methods are adopted such as Markov chain Monte Carlo (MCMC) (Maceachern, 1994; Escobar & West, 1995; Neal, 2000) or variational inference (Blei & Jordan, 2006; Wang & Dunson, 2011; Lin, 2013).”
Variational inference for Dirichlet process mixtures (2006, methodology): “MCMC provides one class of approximations for this posterior and the predictive density (MacEachern 1994; Escobar and West 1995; Neal 2000).”
Posterior Analysis for Normalized Random Measures with Independent Increments (2009, background): “See Pitman (2006) and Ishwaran and James (2003).”
Bayesian infinite mixture model based clustering of gene expression profiles (2002, background): “Potential bene fits of running several shorter runs instead of a single long Gibbs sampler, as well as advantages of using potentially more efficient Gibbs sampling scheme (e.g. Jain and Neal, 2000; MacEachern, 1994 ) need to be further investigated.”
An ANOVA Model for Dependent Random Measures (2004, background): “This distribution is useful for marginalizing the mass parameter through preintegration,as described in MacEachern (1998).”
Marginal Likelihood and Bayes Factors for Dirichlet Process Mixture Models (2003, methodology): “For the conjugate case, MacEachern (1994) developed an improved collapsed Gibbs sampler that provides better mixing.”
Semiparametric Bayesian Inference of Long‐Memory Stochastic Volatility Models (2004, methodology): “Like our Bayesian estimator, the GPH is a semiparametric estimator of d but is based on the log-periodogram regression. Because log y(2) t is not Gaussian, the asymptotic properties found by Robinson (1995) for the GPH estimator do not hold under the assumptions of the LMSV model.”
Mixture Modeling for Marked Poisson Processes (2010, methodology): “from the conditional posterior distribution for G(obtained as discussed in Section 4.2). A generic Gibbs sampler for posterior simulation from p(?;s;; jdata), derived by combining MCMC methods from MacEachern (1994) and Escobar and West (1995), proceeds iteratively as follows: For i= 1;:::;N, denote”
DIRICHLET PRIOR SIEVES IN FINITE NORMAL MIXTURES (2002, methodology): “The wide scope of applications has led to many different methods for fitting this model, including Monte Carlo simulation (Lo (1984), Kuo (1986), Ferguson (1983)), and Markov chain Monte Carlo Gibbs sampling (Escobar (1988, 1994), MacEachern (1994), Escobar and West (1995)).”
Speaker Diarization: A Review of Recent Research (2010, methodology): “used [20] to provide a systematic approach to the computatio n of distributions via sampling, enabling the deployment of Bayesian methods.”
Bayesian density regression (2007, methodology): “We propose a data augmentation MCMC algorithm for posterior computation, relying on a generalization of the accelerated Pólya urn Gibbs sampler (MacEachern, 1994; West et al., 1994; Escobar and West, 1998).”
Joint Species Distribution Modeling: Dimension Reduction Using Dirichlet Processes (2017, methodology): “We use a stick-breaking representation based upon MacEachern (1994) which defines a dependent Dirichlet process (see also Dunson and Park (2008); Chung and Dunson (2011)).”
Kernel Bayes' rule: Bayesian inference with positive definite kernels (2013, methodology): “Approximate Bayesian Computation (ABC) is a popular method for approximately sampling from a posterior without knowing the functional form (Marjoram et al., 2003; Sisson et al., 2007; Tavaré et al., 1997). – Another interesting application along these lines is nonparametric Bayesian inference (Mülle”
Martingale posterior distributions (2021, background): “Computation for the multivariate DPMM (MacEachern, 1994; Escobar and West, 1995; Neal, 2000) may scale poorly as the number of dimensions grows.”
Generalized weighted Chinese restaurant processes for species sampling mixture models (2003, methodology): “For example, MacEachern (1994) (see also MacEachern and Müller (1998)) note that a collapsed Gibbs sampler based on partitions leads to improved mixing over Pólya urn Gibbs samplers in Dirichlet process mixture models.”
Hierarchical priors and mixture models, with applications in regression and density estimation (2006, background): “Conditional on k; introduce indicators Si = j if i = j so that, given Si = j and ; yi Fi(:j j): The con guration S = fS1; : : : ;Sng (West 1990; MacEachern 1992) determines a one-way classi cation of the data Y = fy1; : : : ; yng into k distinct groups or clusters; the nj = #fSi = jg observations in”