Variational approximations in Bayesian model selection for finite mixture distributions

By Clare A. McGrory, D. M. Titterington. 2007

180 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Statistics and Probability

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Citations in the corpus

Variational Inference: A Review for Statisticians (2016, neutral): “This has been explored for mixture models (Ueda and Ghahramani 2002; McGrory and Titterington 2007) and more generally (Beal and Ghahramani 2003).”
Mean field variational bayes for elaborate distributions (2011, methodology): “It was developed as a methodology for inference in graphical models in the late 1990s (e.g., Jordan et al. 1999, Attias 1999) and during the 2000s it per-meated further into the mainstream statistical literature (e.g., Teschendorﬀ et al. 2005; McGrory and Titterington 2007).”
Posterior Analysis for Normalized Random Measures with Independent Increments (2009, background): “Note that the prediction rule reduces to the one provided by Pitman (2003) in the homogeneous case.”
Mean field variational Bayes for continuous sparse signal shrinkage: Pitfalls and remedies (2014, unknown): “[23] McGrory, C.A. and Titterington, D.M. (2007).”
Asymptotic behaviour of the posterior distribution in overfitted mixture models (2011, background): “…in a mixture distribution has entertained and concerned a large number of researchers and attracted a correspondingly large literature (Akaike, 1973; Dempster et al., 1977; Lee et al., 2008; McGrory and Titterington, 2007; Richardson and Green, 1997; Robert and Wraith, 2009; Schwarz, 1978).”
Model-Based Clustering (2016, methodology): “The variational Bayes algorithm has already been applied to Gaussian mixture models (e.g., Teschendorff et al. 2005; McGrory and Titterington 2007; Subedi and McNicholas 2016) as well as non-Gaussian mixtures (e.g., Subedi and McNicholas 2014).”
Explaining Variational Approximations (2010, background): “See, for example, Bishop (2006, Section 10.2.4) and McGrory & Titterington (2007).”
Regression Density Estimation With Variational Methods and Stochastic Approximation (2012, methodology): “McGrory and Titterington (2007) considered a variational extension of the deviance information criterion (DIC) (Spiegelhalter et al. 2002) and a variational optimization technique where the algorithm is initialized with a large number of components and mixture components, whose weightings become…”