Sampling the Dirichlet Mixture Model with Slices

By Stephen G. Walker. Communications in statistics. Simulation and computation, 2007

503 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Mathematical Physics, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/0337920f287e25aa00c304f5ab6f9fb67e33c7d4

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Citations in the corpus

The Nested Dirichlet Process (2008, methodology): “Potentially, a finite approximation can be avoided by relying on a slice sampler that generalizes the algorithm of Walker (2007), although we have not yet implemented this approach.”
Bayesian inference with dependent normalized completely random measures (2014, background): “, the slice sampler [38] and the retrospective sampler [31].”
Nonparametric Bayesian Multiple Imputation for Incomplete Categorical Variables in Large-Scale Assessment Surveys (2013, methodology): “We note that one can use other MCMC algorithms to estimate the posterior distribution that avoid truncation, for example a slice sampler (Dunson & Xing, 2009; Walker, 2007) or an exact blocked sampler (Papaspiliopoulos, 2008).”
Bayesian semiparametric modeling of realized covariance matrices (2016, methodology): “2 Posterior inference To sample from the posterior we use slice sampling techniques introduced by Walker (2007) and extended by Kalli et al.”
Bayesian forecasting in economics and finance: A modern review (2022, methodology): “The advancement of the literature in this direction has been aided by the stick-breaking representation (Sethuraman, 1994) and the introduction of the slice sampler (Walker, 2007; Kalli et al., 2011).”
A Sticky HDP-HMM With Application to Speaker Diarization (2009, methodology): “The beam sampler was inspired by a related approach for DP mixture models (Walker, 2007), which is conceptually similar to retrospective sampling methods (Papaspiliopoulos and Roberts, 2008).”
A Flexible Bayesian Nonparametric Model for Predicting Future Insurance Claims (2017, unknown): “In particular, Escobar and West (1995), MacEachern and Müller (1998), Walker (2007), and Kalli et al. (2011) have explored the computation of the posterior for f using Markov chain Monte Carlo.”
Bayesian Nonparametric Calibration and Combination of Predictive Distributions (2015, methodology): “In this article, we apply the slice sampling algorithm proposed by Walker (2007) and Kalli, Griffin, and Walker (2011).”
Bayesian Nonparametrics: Hierarchical Bayesian nonparametric models with applications (2010, background): “(2008) proposed a slice sampling approach which adaptively limits the number of states to a finite number (Neal, 2003; Walker, 2007).”
Probabilistic grammars and hierarchical Dirichlet processes (2018, background): “stick-breaking representation, one must deal with the infinite-dimensional parameters by either truncating the stick (Ishwaran and James, 2001), introducing auxiliary variables that effectively provide an adaptive truncation (Walker, 2004), or adaptively allocating more memory for new components (Pa”
Comparing distributions by using dependent normalized random‐measure mixtures (2013, methodology): “Some details of simulating the jumps are given in Griffin and Walker (2011). 18”
Bayesian Inference Methods for Univariate and Multivariate GARCH Models: A Survey (2014, unknown): “…schemes to deal with the inﬁniteness in DPM, and the retrospective MCMC method of Papaspiliopoulos & Roberts (2008), that is later combined by Papaspiliopoulos (2008) with slice sampling method by Walker (2007) to obtain a new composite algorithm, which is better, faster and easier to implement.”
A Bayesian Semiparametric Approach to Intermediate Variables in Causal Inference (2011, methodology): “Exact sampling from the DPM models can be obtained using the algorithms recently developed by, among others, Walker (2007) and Papaspiliopoulos and Roberts (2008).”
POSTERIOR CONSISTENCY IN CONDITIONAL DENSITY ESTIMATION BY COVARIATE DEPENDENT MIXTURES (2011, methodology): “To estimate the model, we develop an MCMC algorithm based on slice sampling (Neal, 2003; Walker, 2007) and retrospective sampling (Papaspiliopoulos and Roberts, 2008; Papaspiliopoulos, 2008).”
Beta Processes, Stick-Breaking and Power Laws (2011, background): “…Dirichlet process at the center of Bayesian nonparamet-rics, driving the development of a wide variety of inference algorithms for models based on Dirichlet process priors and suggesting a range of generalizations [e.g. MacEachern, 1999, Ishwaran and James, 2001, Walker, 2007, Kalli et al., 2009].”
Slice sampling mixture models (2011, methodology): “The “slice–efficient” which is the one described above and the “slice” which is the original algorithm appearing in Walker (2007) and is noted by the fact that the v is sampled conditional on u in this case.”
Bayesian Nonparametrics: Models beyond the Dirichlet process (2009, unknown): “A few details on this can be found in Walker and Muliere (1997) and Walker and Damien (1998).”
Tree-Structured Stick Breaking for Hierarchical Data (2010, methodology): “To avoid this difficulty, we use a slice sampling approach that can be viewed as a combination of the Dirichlet slice sampler of Walker [20] and the retrospective sampler of Papaspiliopolous and Roberts [21].”
On the stick-breaking representation of normalized inverse Gaussian priors (2012, background): “…are the blocked Gibbs sampler (Ishwaran & James, 2001), the retrospective sampler (Papaspiliopoulos & Roberts, 2008), the slice sampler (Walker, 2007) and a very efficient synthesis (Yau et al., 2011) of these last two. at B row n U niversity on O ctober 1, 2012…”