Graphical models for non-negative data using generalized score matching

Shiqing Yu, Mathias Drton, Ali Shojaie

Research output: Contribution to conferencePaperpeer-review

9 Scopus citations

Abstract

A common challenge in estimating parameters of probability density functions is the intractability of the normalizing constant. While in such cases maximum likelihood estimation may be implemented using numerical integration, the approach becomes computationally intensive. In contrast, the score matching method of Hyvärinen (2005) avoids direct calculation of the normalizing constant and yields closed-form estimates for exponential families of continuous distributions over ℝm. Hyvärinen (2007) extended the approach to distributions supported on the non-negative orthant ℝm+ . In this paper, we give a generalized form of score matching for non-negative data that improves estimation efficiency. We also generalize the regularized score matching method of Lin et al. (2016) for non-negative Gaussian graphical models, with improved theoretical guarantees.

Original languageEnglish
Pages1781-1790
Number of pages10
StatePublished - 2018
Externally publishedYes
Event21st International Conference on Artificial Intelligence and Statistics, AISTATS 2018 - Playa Blanca, Lanzarote, Canary Islands, Spain
Duration: 9 Apr 201811 Apr 2018

Conference

Conference21st International Conference on Artificial Intelligence and Statistics, AISTATS 2018
Country/TerritorySpain
CityPlaya Blanca, Lanzarote, Canary Islands
Period9/04/1811/04/18

Fingerprint

Dive into the research topics of 'Graphical models for non-negative data using generalized score matching'. Together they form a unique fingerprint.

Cite this