Auxiliary Variables for Multi-Dirichlet Priors
Bayesian models that mix multiple Dirichlet prior parameters, called Multi-Dirichlet priors (MD) in this paper, are gaining popularity. Inferring mixing weights and parameters of mixed prior distributions seems tricky, as sums over Dirichlet parameters complicate the joint distribution of model para...
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| creator | Kling, Christoph Carl |
| description | Bayesian models that mix multiple Dirichlet prior parameters, called
Multi-Dirichlet priors (MD) in this paper, are gaining popularity. Inferring
mixing weights and parameters of mixed prior distributions seems tricky, as
sums over Dirichlet parameters complicate the joint distribution of model
parameters.
This paper shows a novel auxiliary variable scheme which helps to simplify
the inference for models involving hierarchical MDs and MDPs. Using this
scheme, it is easy to derive fully collapsed inference schemes which allow for
an efficient inference. |
| doi_str_mv | 10.48550/arxiv.1708.05257 |
| format | Article |
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Multi-Dirichlet priors (MD) in this paper, are gaining popularity. Inferring
mixing weights and parameters of mixed prior distributions seems tricky, as
sums over Dirichlet parameters complicate the joint distribution of model
parameters.
This paper shows a novel auxiliary variable scheme which helps to simplify
the inference for models involving hierarchical MDs and MDPs. Using this
scheme, it is easy to derive fully collapsed inference schemes which allow for
an efficient inference.</description><identifier>DOI: 10.48550/arxiv.1708.05257</identifier><language>eng</language><subject>Statistics - Machine Learning</subject><creationdate>2017-08</creationdate><rights>http://creativecommons.org/licenses/by/4.0</rights><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>228,230,780,885</link.rule.ids><linktorsrc>$$Uhttps://arxiv.org/abs/1708.05257$$EView_record_in_Cornell_University$$FView_record_in_$$GCornell_University$$Hfree_for_read</linktorsrc><backlink>$$Uhttps://doi.org/10.48550/arXiv.1708.05257$$DView paper in arXiv$$Hfree_for_read</backlink></links><search><creatorcontrib>Kling, Christoph Carl</creatorcontrib><title>Auxiliary Variables for Multi-Dirichlet Priors</title><description>Bayesian models that mix multiple Dirichlet prior parameters, called
Multi-Dirichlet priors (MD) in this paper, are gaining popularity. Inferring
mixing weights and parameters of mixed prior distributions seems tricky, as
sums over Dirichlet parameters complicate the joint distribution of model
parameters.
This paper shows a novel auxiliary variable scheme which helps to simplify
the inference for models involving hierarchical MDs and MDPs. Using this
scheme, it is easy to derive fully collapsed inference schemes which allow for
an efficient inference.</description><subject>Statistics - Machine Learning</subject><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2017</creationdate><recordtype>article</recordtype><sourceid>GOX</sourceid><recordid>eNotzssKwjAUBNBsXIj6Aa7sD7QmzeMmy-IbFF2I25KmLV6IVFIV_XvrYzUMDMMhZMxoIrSUdGrDEx8JA6oTKlMJfZJk9yd6tOEVnWxAW_iqjeomRLu7v2E8x4Du7KtbdAjYhHZIerX1bTX654Acl4vjbB1v96vNLNvGVgHEJQeuDBNGidpx6TQvRAk6VaWAkgpXVDTVulsypurCSO7AdI2nGqyVyvABmfxuv-D8GvDSCfMPPP_C-RtMaTvU</recordid><startdate>20170817</startdate><enddate>20170817</enddate><creator>Kling, Christoph Carl</creator><scope>EPD</scope><scope>GOX</scope></search><sort><creationdate>20170817</creationdate><title>Auxiliary Variables for Multi-Dirichlet Priors</title><author>Kling, Christoph Carl</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-a677-d3736914964fc35c83b4d7826d47d04cbe0288677116fb953c796773287aa5693</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2017</creationdate><topic>Statistics - Machine Learning</topic><toplevel>online_resources</toplevel><creatorcontrib>Kling, Christoph Carl</creatorcontrib><collection>arXiv Statistics</collection><collection>arXiv.org</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Kling, Christoph Carl</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Auxiliary Variables for Multi-Dirichlet Priors</atitle><date>2017-08-17</date><risdate>2017</risdate><abstract>Bayesian models that mix multiple Dirichlet prior parameters, called
Multi-Dirichlet priors (MD) in this paper, are gaining popularity. Inferring
mixing weights and parameters of mixed prior distributions seems tricky, as
sums over Dirichlet parameters complicate the joint distribution of model
parameters.
This paper shows a novel auxiliary variable scheme which helps to simplify
the inference for models involving hierarchical MDs and MDPs. Using this
scheme, it is easy to derive fully collapsed inference schemes which allow for
an efficient inference.</abstract><doi>10.48550/arxiv.1708.05257</doi><oa>free_for_read</oa></addata></record> |
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| subjects | Statistics - Machine Learning |
| title | Auxiliary Variables for Multi-Dirichlet Priors |
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