Angaben zur Quelle [Bearbeiten]
Autor | Carl Edward Rasmussen, Bernard J de la Cruz, Zoubin Ghahramani, David L Wild |
Titel | Modeling and Visualizing Uncertainty in Gene Expression Clusters using Dirichlet Process Mixtures |
Herausgeber | IEEE Computer Society |
Datum | 29. November 2007 |
Seiten | 1-29 |
Anmerkung | Dieser preprint wurde dann in 2009 publiziert: http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=4407680 |
URL | http://www.kyb.tuebingen.mpg.de/fileadmin/user_upload/files/publications/IEEE-ACM-Trans-Comp-Biol-Bioinf-Rasmussen_4799[0].pdf |
Literaturverz. |
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Fußnoten | nein |
Fragmente | 3 |
Fragmente der Quelle:
[1.] Rh/Fragment 136 19 - Diskussion Zuletzt bearbeitet: 2012-08-15 21:56:38 Hindemith | Fragment, Rasmussen et al. 2007, Rh, SMWFragment, Schutzlevel, Verschleierung, ZuSichten |
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Untersuchte Arbeit: Seite: 136, Zeilen: 19-22 |
Quelle: Rasmussen et al. 2007 Seite(n): 3, Zeilen: 10-13 |
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An important issue that must be addressed in M4 processes is the question of how wide the cluster size coefficent K, (the serial dependence) and patterns L (the different forms or shapes of the clusters) are to use [sic]. Bayesian statistics and model based approaches can provide elegant solutions to model selection questions of this kind. | An important issue that must be addressed in any clustering method is the question of how many clusters to use. Bayesian statistics and model based approaches can provide elegant solutions to model selection questions of this kind. |
Keine Quelle angegeben. Während der erste Satz noch stark umgeschrieben ist (wohl um die Thematik anzupassen), ist der zweite Satz wörtlich übernommen. Siehe auch die Diskussionsseite. Insbesondere vgl. auch Rh/Fragment 137 02 und Rh/Fragment 137 05. |
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[2.] Rh/Fragment 137 02 - Diskussion Zuletzt bearbeitet: 2012-07-30 23:03:47 Kybot | Fragment, Gesichtet, KeineWertung, Rasmussen et al. 2007, Rh, SMWFragment, Schutzlevel |
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Untersuchte Arbeit: Seite: 137, Zeilen: 1-2 |
Quelle: Rasmussen et al. 2007 Seite(n): 3, Zeilen: 16-17 |
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Within a Bayesian framework, all assumptions are presented in terms of priors and the choice of a likelihood function. | Within a Bayesian framework, all assumptions are presented in terms of priors and the choice of likelihood function. |
Sehr kurz, daher "keine Wertung". |
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[3.] Rh/Fragment 137 05 - Diskussion Zuletzt bearbeitet: 2012-07-30 23:32:04 Hindemith | Fragment, Gesichtet, Rasmussen et al. 2007, Rh, SMWFragment, Schutzlevel sysop, Verschleierung |
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Untersuchte Arbeit: Seite: 137, Zeilen: 5-13 |
Quelle: Rasmussen et al. 2007 Seite(n): 3, Zeilen: 22ff |
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We describe an approach to the problem of automatically determine [sic] the number patterns L based on the theory of infinite Gaussian mixtures or Dirichlet process mixtures. This theory is based on the observation that the mathematical limit of an infinite number of components in an ordinary finite mixture model (i.e. the patterns L in the d-dimensional model) corresponds to a Dirichlet process prior. In an infinite Gaussian mixture model there is no need to make arbitrary choices about how many patterns L there are in the process. The major advantage is that, although in theory the infinite mixture model has an infinite number of parameters, it is possible to do exact inference in these infinite mixture models efficiently using Markov chain Monte Carlo (MCMC) methodology. | We describe an approach to the problem of automatically clustering microarray gene expression profiles based on the theory of infinite Gaussian mixtures (or Dirichlet process mixtures (DPM)) [5], [6]. This theory is based on the observation that the mathematical limit of an infinite number of components in an ordinary finite mixture model (i.e. clustering model) corresponds to a Dirichlet process prior [5]–[7]. In an infinite Gaussian mixture model there is no need to make arbitrary choices about how many clusters there are in the data. Although in theory the infinite mixture model has an infinite number of parameters, surprisingly, it is possible to do exact inference in these infinite mixture models efficiently using Markov chain Monte Carlo (MCMC) methodology, [...] |
Die Quelle wird nicht erwähnt. Zur Anpassung des Untersuchungsgegenstandes werden einige Stichwortersetzungen vorgenommen. Dabei scheinen sich im ersten Satz sprachliche Fehler eingeschlichen zu haben. |
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