Bayesian model search for mixture models based on optimizing variational bounds

By Naonori Ueda, Zoubin Ghahramani. 2002

161 citations recorded by the source corpus.

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In Artificial Intelligence

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).”
Variational Bayesian Grammar Induction for Natural Language (2006, unknown): “The variational Bayes (VB) [1, 6] has succeeded in many applications [12, 17].”
Variational approximations in Bayesian model selection for finite mixture distributions (2007, methodology): “Variational methods are a fast, deterministic alternative to MCMC methods and recently they have been gaining popularity in the machine learning literature (for instance, Jordan et al. (1999), Corduneanu and Bishop (2001) and Ueda and Ghahramani (2002)).”
Inference for Subgroup Analysis With a Structured Logistic-Normal Mixture Model (2015, methodology): “Bayesian methods for selecting the number of experts were suggested by Peng, Jacobs, and Tanner (1996) and Ueda and Ghahramani (2002), whereas Fritsch, Finke, and Waibel (1997) considered a grow-and-prune strategy of the model for the same purpose.”
Regression Density Estimation With Variational Methods and Stochastic Approximation (2012, methodology): “They discussed sophisticated split-and-merge algorithms, building on earlier related methods, such as those in Ghahramani and Beal (2000), Ueda and Ghahramani (2002), and Constantinopoulos and Likas (2007), for simultaneous model selection and parameter estimation as well as on novel criteria for…”
Bounded Approximations for Marginal Likelihoods (2010, methodology): “…approximation of marginal distributions (MacKay, 1995; Jordan et al., 1999; Jaakkola and Jordan, 2000; Humphreys and Titterington, 2000; Ueda and Ghahramani, 2002; Jordan, 2004; Wang and Titterington, 2004), have been implemented in the model selection context (Corduneanu and Bishop,…”
Latent-Space Variational Bayes (2008, methodology): “…not have the same asymptotic guarantees as MCMC. Recently, Variational Bayesian Expectation-Maximization (VBEM) [13], [14] has become one of the standard VB approximate inference methods and has been successfully applied to many interesting latent variable models [14], [15], [16], [17], [18], [19].”
Twenty Years of Mixture of Experts (2012, methodology): “In doing so, instead of trying to find the number of experts or specifying a number, Rasmussen and Ghahramani assumed an infinite number of experts, most of which contributed only a small mass to the distribution.”
Competitive Coevolution through Evolutionary Complexification (2011, methodology): “For example, Bayesian methods have been applied to learning model structure (Attias, 2000; Ueda & Ghahramani, 2002 ).”
Convergence properties of a general algorithm for calculating variational Bayesian estimates for a normal mixture model (2006, background): “…graphical models (Attias (1999, 2000)), mixture models (Humphreys and Titterington (2000); Penny and Roberts (2000); Corduneanu and Bishop (2001); Ueda and Ghahramani (2003)), mixtures of factor analysers (Ghahramani and Beal (2000)) and state space models (Ghahramani and Beal (2001); Beal…”
Bayesian Hierarchical Mixtures of Experts (2002, methodology): “For larger trees, we could consider using greedy search algorithms (Ueda and Ghahramani 2002) or Monte-Carlo methods (Chipman, George, and McCulloch 2002).”