Matches in SemOpenAlex for { <https://semopenalex.org/work/W2127498532> ?p ?o ?g. }
Showing items 1 to 96 of
96
with 100 items per page.
- W2127498532 abstract "Dirichlet process (DP) mixture models are the cornerstone of nonparametric Bayesian statistics, and the development of Monte-Carlo Markov chain (MCMC) sampling methods for DP mixtures has enabled the application of nonparametric Bayesian methods to a variety of practical data analysis problems. However, MCMC sampling can be prohibitively slow, and it is important to explore alternatives. One class of alternatives is provided by variational methods, a class of deterministic algorithms that convert inference problems into optimization problems (Opper and Saad 2001; Wainwright and Jordan 2003). Thus far, variational methods have mainly been explored in the parametric setting, in particular within the formalism of the exponential family (Attias 2000; Ghahramani and Beal 2001; Blei et al. 2003). In this paper, we present a variational inference algorithm for DP mixtures. We present experiments that compare the algorithm to Gibbs sampling algorithms for DP mixtures of Gaussians and present an application to a large-scale image analysis problem." @default.
- W2127498532 created "2016-06-24" @default.
- W2127498532 creator A5049812527 @default.
- W2127498532 creator A5070920982 @default.
- W2127498532 date "2006-03-01" @default.
- W2127498532 modified "2023-10-18" @default.
- W2127498532 title "Variational inference for Dirichlet process mixtures" @default.
- W2127498532 cites W1516111018 @default.
- W2127498532 cites W1967687583 @default.
- W2127498532 cites W2053405531 @default.
- W2127498532 cites W2055325763 @default.
- W2127498532 cites W2069429561 @default.
- W2127498532 cites W2070612147 @default.
- W2127498532 cites W2072169887 @default.
- W2127498532 cites W2089484716 @default.
- W2127498532 cites W2091797506 @default.
- W2127498532 cites W2137918516 @default.
- W2127498532 cites W2141177889 @default.
- W2127498532 cites W4231458249 @default.
- W2127498532 cites W4235499294 @default.
- W2127498532 cites W4292691288 @default.
- W2127498532 doi "https://doi.org/10.1214/06-ba104" @default.
- W2127498532 hasPublicationYear "2006" @default.
- W2127498532 type Work @default.
- W2127498532 sameAs 2127498532 @default.
- W2127498532 citedByCount "1212" @default.
- W2127498532 countsByYear W21274985322012 @default.
- W2127498532 countsByYear W21274985322013 @default.
- W2127498532 countsByYear W21274985322014 @default.
- W2127498532 countsByYear W21274985322015 @default.
- W2127498532 countsByYear W21274985322016 @default.
- W2127498532 countsByYear W21274985322017 @default.
- W2127498532 countsByYear W21274985322018 @default.
- W2127498532 countsByYear W21274985322019 @default.
- W2127498532 countsByYear W21274985322020 @default.
- W2127498532 countsByYear W21274985322021 @default.
- W2127498532 countsByYear W21274985322022 @default.
- W2127498532 countsByYear W21274985322023 @default.
- W2127498532 crossrefType "journal-article" @default.
- W2127498532 hasAuthorship W2127498532A5049812527 @default.
- W2127498532 hasAuthorship W2127498532A5070920982 @default.
- W2127498532 hasBestOaLocation W21274985321 @default.
- W2127498532 hasConcept C102366305 @default.
- W2127498532 hasConcept C105795698 @default.
- W2127498532 hasConcept C107673813 @default.
- W2127498532 hasConcept C111350023 @default.
- W2127498532 hasConcept C11413529 @default.
- W2127498532 hasConcept C134306372 @default.
- W2127498532 hasConcept C154945302 @default.
- W2127498532 hasConcept C158424031 @default.
- W2127498532 hasConcept C160234255 @default.
- W2127498532 hasConcept C169214877 @default.
- W2127498532 hasConcept C182310444 @default.
- W2127498532 hasConcept C2776214188 @default.
- W2127498532 hasConcept C2781280628 @default.
- W2127498532 hasConcept C28826006 @default.
- W2127498532 hasConcept C33923547 @default.
- W2127498532 hasConcept C41008148 @default.
- W2127498532 hasConcept C55974624 @default.
- W2127498532 hasConceptScore W2127498532C102366305 @default.
- W2127498532 hasConceptScore W2127498532C105795698 @default.
- W2127498532 hasConceptScore W2127498532C107673813 @default.
- W2127498532 hasConceptScore W2127498532C111350023 @default.
- W2127498532 hasConceptScore W2127498532C11413529 @default.
- W2127498532 hasConceptScore W2127498532C134306372 @default.
- W2127498532 hasConceptScore W2127498532C154945302 @default.
- W2127498532 hasConceptScore W2127498532C158424031 @default.
- W2127498532 hasConceptScore W2127498532C160234255 @default.
- W2127498532 hasConceptScore W2127498532C169214877 @default.
- W2127498532 hasConceptScore W2127498532C182310444 @default.
- W2127498532 hasConceptScore W2127498532C2776214188 @default.
- W2127498532 hasConceptScore W2127498532C2781280628 @default.
- W2127498532 hasConceptScore W2127498532C28826006 @default.
- W2127498532 hasConceptScore W2127498532C33923547 @default.
- W2127498532 hasConceptScore W2127498532C41008148 @default.
- W2127498532 hasConceptScore W2127498532C55974624 @default.
- W2127498532 hasIssue "1" @default.
- W2127498532 hasLocation W21274985321 @default.
- W2127498532 hasLocation W21274985322 @default.
- W2127498532 hasOpenAccess W2127498532 @default.
- W2127498532 hasPrimaryLocation W21274985321 @default.
- W2127498532 hasRelatedWork W1480633635 @default.
- W2127498532 hasRelatedWork W2057511657 @default.
- W2127498532 hasRelatedWork W2071940340 @default.
- W2127498532 hasRelatedWork W2127498532 @default.
- W2127498532 hasRelatedWork W2498662104 @default.
- W2127498532 hasRelatedWork W2575736411 @default.
- W2127498532 hasRelatedWork W2952124557 @default.
- W2127498532 hasRelatedWork W2979578718 @default.
- W2127498532 hasRelatedWork W3123716841 @default.
- W2127498532 hasRelatedWork W197558907 @default.
- W2127498532 hasVolume "1" @default.
- W2127498532 isParatext "false" @default.
- W2127498532 isRetracted "false" @default.
- W2127498532 magId "2127498532" @default.
- W2127498532 workType "article" @default.