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 language | English |
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Pages | 1781-1790 |
Number of pages | 10 |
State | Published - 2018 |
Externally published | Yes |
Event | 21st International Conference on Artificial Intelligence and Statistics, AISTATS 2018 - Playa Blanca, Lanzarote, Canary Islands, Spain Duration: 9 Apr 2018 → 11 Apr 2018 |
Conference
Conference | 21st International Conference on Artificial Intelligence and Statistics, AISTATS 2018 |
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Country/Territory | Spain |
City | Playa Blanca, Lanzarote, Canary Islands |
Period | 9/04/18 → 11/04/18 |