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Posted to dev@mahout.apache.org by "Isabel Drost (JIRA)" <ji...@apache.org> on 2008/04/08 16:15:24 UTC
[jira] Created: (MAHOUT-29) Gibb's sampling implementation
Gibb's sampling implementation
------------------------------
Key: MAHOUT-29
URL: https://issues.apache.org/jira/browse/MAHOUT-29
Project: Mahout
Issue Type: New Feature
Reporter: Isabel Drost
Copied over from original issue:
> EM clustering can also be changed very slightly by assigning points to single clusters chosen at random according to the probability of membership. This
> turns EM clustering into Gibb's sampling. The important property that is changed is that you now can sample over the distribution of possible samplings
> which can be very important if some parts of your data are well defined and some parts not so well defined.
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[jira] Commented: (MAHOUT-29) Gibb's sampling implementation
Posted by "Ted Dunning (JIRA)" <ji...@apache.org>.
[ https://issues.apache.org/jira/browse/MAHOUT-29?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=12586848#action_12586848 ]
Ted Dunning commented on MAHOUT-29:
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The umacs package in R provides a very nice implementation of an integrated Gibbs and Metropolis framework.
http://cran.r-project.org/web/packages/Umacs/index.html
> Gibb's sampling implementation
> ------------------------------
>
> Key: MAHOUT-29
> URL: https://issues.apache.org/jira/browse/MAHOUT-29
> Project: Mahout
> Issue Type: New Feature
> Reporter: Isabel Drost
>
> Copied over from original issue:
> > EM clustering can also be changed very slightly by assigning points to single clusters chosen at random according to the probability of membership. This
> > turns EM clustering into Gibb's sampling. The important property that is changed is that you now can sample over the distribution of possible samplings
> > which can be very important if some parts of your data are well defined and some parts not so well defined.
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[jira] Resolved: (MAHOUT-29) Gibb's sampling implementation
Posted by "Sean Owen (JIRA)" <ji...@apache.org>.
[ https://issues.apache.org/jira/browse/MAHOUT-29?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Sean Owen resolved MAHOUT-29.
-----------------------------
Resolution: Won't Fix
> Gibb's sampling implementation
> ------------------------------
>
> Key: MAHOUT-29
> URL: https://issues.apache.org/jira/browse/MAHOUT-29
> Project: Mahout
> Issue Type: New Feature
> Reporter: Isabel Drost
>
> Copied over from original issue:
> > EM clustering can also be changed very slightly by assigning points to single clusters chosen at random according to the probability of membership. This
> > turns EM clustering into Gibb's sampling. The important property that is changed is that you now can sample over the distribution of possible samplings
> > which can be very important if some parts of your data are well defined and some parts not so well defined.
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