Poisson Dependency Networks: Gradient Boosted Models for Multivariate Count Data
Abstract
Although count data are increasingly ubiquitous, surprisingly little work has employed probabilistic graphical models for modeling count data. Indeed the univariate case has been well studied, however, in many situations counts influence each other and should not be considered independently. Standard graphical models such as multinomial or Gaussian ones are also often ill-suited, too, since they disregard either the infinite range over the...
Paper Details
Title
Poisson Dependency Networks: Gradient Boosted Models for Multivariate Count Data
Published Date
Jul 11, 2015
Journal
Volume
100
Issue
2-3
Pages
477 - 507
Citation AnalysisPro
You’ll need to upgrade your plan to Pro
Looking to understand the true influence of a researcher’s work across journals & affiliations?
- Scinapse’s Top 10 Citation Journals & Affiliations graph reveals the quality and authenticity of citations received by a paper.
- Discover whether citations have been inflated due to self-citations, or if citations include institutional bias.
Notes
History