Properties and Bayesian fitting of restricted Boltzmann machines Kaplan, Andee Nordman, Daniel Vardeman, Stephen
dc.contributor.department Statistics
dc.contributor.department Industrial and Manufacturing Systems Engineering 2020-02-28T18:29:01.000 2020-07-02T06:57:40Z 2020-07-02T06:57:40Z Mon Jan 01 00:00:00 UTC 2018 2020-02-01 2019-02-01
dc.description.abstract <p>A restricted Boltzmann machine (RBM) is an undirected graphical model constructed for discrete or continuous random variables, with two layers, one hidden and one visible, and no conditional dependency within a layer. In recent years, RBMs have risen to prominence due to their connection to deep learning. By treating a hidden layer of one RBM as the visible layer in a second RBM, a deep architecture can be created. RBMs thereby are thought to have the ability to encode very complex and rich structures in data, making them attractive for supervised learning. However, the generative behavior of RBMs largely is unexplored and typical fitting methodology does not easily allow for uncertainty quantification in addition to point estimates. In this paper, we discuss the relationship between RBM parameter specification in the binary case and model properties such as degeneracy, instability and uninterpretability. We also describe the associated difficulties that can arise with likelihood‐based inference and further discuss the potential Bayes fitting of such (highly flexible) models, especially as Gibbs sampling (quasi‐Bayes) methods often are advocated for the RBM model structure.</p>
dc.description.comments <p>This is the peer-reviewed version of the following article: Kaplan, Andee, Daniel Nordman, and Stephen Vardeman. "Properties and Bayesian fitting of restricted Boltzmann machines." <em>Statistical Analysis and Data Mining: The ASA Data Science Journal</em> 12, no. 1 (2019): 23-38, which has been published in final form at DOI: <a href="" target="_blank">10.1002/sam.11396</a>. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving.</p>
dc.format.mimetype application/pdf
dc.identifier archive/
dc.identifier.articleid 1290
dc.identifier.contextkey 15787868
dc.identifier.s3bucket isulib-bepress-aws-west
dc.identifier.submissionpath stat_las_pubs/288
dc.language.iso en
dc.source.bitstream archive/|||Fri Jan 14 23:12:07 UTC 2022
dc.subject.disciplines Probability
dc.subject.disciplines Statistical Models
dc.subject.disciplines Statistics and Probability
dc.subject.keywords Degeneracy
dc.subject.keywords Instability
dc.subject.keywords Classification
dc.subject.keywords Deep Learning
dc.subject.keywords Graphical Models
dc.title Properties and Bayesian fitting of restricted Boltzmann machines
dc.type article
dc.type.genre article
dspace.entity.type Publication
relation.isOrgUnitOfPublication 264904d9-9e66-4169-8e11-034e537ddbca
relation.isOrgUnitOfPublication 51d8b1a0-5b93-4ee8-990a-a0e04d3501b1
Original bundle
Now showing 1 - 1 of 1
1.06 MB
Adobe Portable Document Format