Bayesian clustering and product partition models

By Fernando A. Quintana, Pilar Loreto Iglesias. 2003

236 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Statistics and Probability

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Citations in the corpus

Lifelong Generative Modeling (2017, background): “latent-variable posterior approximation: a requirement in many learning scenarios such as clustering [18], compression [19] and unsupervised representation learning [20].”
An algorithm for optimal partitioning of data on an interval (2003, methodology): “Thus while similar algorithms have been previously developed, ours ﬁnds the exact global optimum for any block-additive ﬁtness function, automatically determines the number of segments, and can be used in either real-time or retrospective analysis.”
Fast Bayesian Inference in Dirichlet Process Mixture Models (2011, methodology): “DPM models have a well-known relationship to partition models (Quintana and Iglesias 2003; Park and Dunson 2009).”
The Dependent Dirichlet Process and Related Models (2020, background): “, Quintana and Iglesias (2003). We introduced the conditional density regression approach assuming endogenous predictors, when the construction of a joint probability model for (yi,xi) is natural.”
Functional clustering by Bayesian wavelet methods (2006, methodology): “(Indeed, Quintana and Iglesias (2003) showed an equivalence under certain regularity conditions on the DP.) This entails the use of two distinct approaches to Gibbs sampling in this paper.”
The Nested Dirichlet Process (2008, background): “Finally, the NDP can be viewed as a way to simultaneously define a prior on a random partition of the collection {G1, . . . ,GJ } (in the style of Quintana and Iglesias 2003) and each of the resulting unique distributions.”
Bayesian Semiparametric Modelling in Quantile Regression (2009, background): “…expression for [ r] involves a sum over all partitions of {1, . . ., n}; the distribution for each sum component is driven by ∗rj i.i.d. from Gr0 for j =1, . . ., n∗r , where n∗r is the number of distinct elements of r corresponding to the specific partition (see, e.g. Quintana & Iglesias, 2003).”
Set Partitioning via Inclusion-Exclusion (2009, unknown): “, [3, 17, 27, 37]).”