ArticleJournal of machine learning research : JMLR2019
Generalized Score Matching for Non-Negative Data.
Article in Journal of machine learning research : JMLR, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
6 citing papers in PubMed.
- Score Matching for Differential Abundance Testing of Compositional High-Throughput Sequencing Data.Statistics in medicine · 2026Article
- Directed Graphical Models and Causal Discovery for Zero-Inflated Data.Proceedings of machine learning research · 2023Article
- Generalized score matching for general domains.Information and inference : a journal of the IMA · 2022Article
- Covariate-Adjusted Inference for Differential Analysis of High-Dimensional Networks.Sankhya. Series A. (2008) · 2022Article
- TORUS GRAPHS FOR MULTIVARIATE PHASE COUPLING ANALYSIS.The annals of applied statistics · 2020Article
- Differential Network Analysis: A Statistical Perspective.Wiley interdisciplinary reviews. Computational statisticsArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
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. 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
Indexed as
Identifiers
34290571PMC8291733What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.