Slice sampling mixture models

By Maria Kalli, Jim E. Griffin, Stephen G. Walker. Statistics and computing, 2011

388 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/0de91b7520de23dc3d9b32fd44559f030119c204

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Citations in the corpus

Compound random measures and their use in Bayesian non‐parametrics (2014, methodology): “This usually leads to computationally tractable methods which either extend random truncation methods such as retrospective sampling (Papaspiliopoulos and Roberts, 2008), or slice sampling (Kalli et al., 2011) or develop truncation ideas for Dirichlet process mixtures (Ishwaran and James, 2001). How”
Bayesian Nonparametric Modeling for Multivariate Ordinal Regression (2014, background): “There exist many Markov chain Monte Carlo (MCMC) algorithms for DP mixtures, including marginal samplers (e.g., Neal 2000), slice sampling algorithms (e.g., Kalli, Griffin, and Walker 2011), and methods that involve truncation of the mixing distribution G (e.g., Ishwaran and James 2001).”
Bayesian semiparametric modeling of realized covariance matrices (2016, methodology): “To sample from the posterior we use slice sampling techniques introduced by Walker (2007) and extended by Kalli et al. (2011) and Papaspiliopoulos (2008).”
A Flexible Bayesian Nonparametric Model for Predicting Future Insurance Claims (2017, methodology): “This version of the slice sampler is called the “independent slice-efficient” algorithm and is described in Section 2 of Kalli et al. (2011).”
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).”
Comparing distributions by using dependent normalized random‐measure mixtures (2013, methodology): “Kalli et al. (2011) developed a more efficient version of this algorithm and extended it to several non-parametric priors.”
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].”
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).”