Polya-gamma augmentations for factor models

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http://hdl.handle.net/10138/153248

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Klami , A 2014 , Polya-gamma augmentations for factor models . in Proceedings of the 6th Asian Conference on Machine Learning . JMLR: Workshop and Conference Proceedings , pp. 112-128 , Asian Conference on Machine Learning , Nha Trang , Viet Nam , 26/11/2014 .

Title: Polya-gamma augmentations for factor models
Author: Klami, Arto
Contributor organization: Helsinki Institute for Information Technology
Multi-source probabilistic inference research group / Arto Klami
Date: 2014-11-26
Language: eng
Number of pages: 16
Belongs to series: Proceedings of the 6th Asian Conference on Machine Learning
Belongs to series: JMLR: Workshop and Conference Proceedings
ISSN: 1938-7228
URI: http://hdl.handle.net/10138/153248
Abstract: Bayesian inference for latent factor models, such as principal component and canonical correlation analysis, is easy for Gaussian likelihoods with conjugate priors using both Gibbs sampling and mean-field variational approximation. For other likelihood potentials one needs to either resort to more complex sampling schemes or to specifying dedicated forms for variational lower bounds. Recently, however, it was shown that for specific likelihoods related to the logistic function it is possible to augment the joint density with auxiliary variables following a P`olya-Gamma distribution, leading to closed-form updates for binary and over-dispersed count models. In this paper we describe how Gibbs sampling and mean-field variational approximation for various latent factor models can be implemented for these cases, presenting easy-to-implement and efficient inference schemas.
Description: Jufo_ID 71804.
Subject: 113 Computer and information sciences
Peer reviewed: Yes
Usage restriction: openAccess
Self-archived version: acceptedVersion


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