Evidence map›Paper›PMID 33850954›Full record

ArticleArXiv2021

A Generalized Unscented Transformation for Probability Distributions.

Donald Ebeigbe, Tyrus Berry, Michael M Norton, Andrew J Whalen, Dan Simon, Timothy Sauer, Steven J Schiff

Open access · greenAbstract readPreprint
PubMed Full text at PMC
In one paragraph

Article in ArXiv, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
0.2field-weighted citation impact, top 54% of its field
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed, 3 citations in OpenAlex.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

7 authors at 3 institutions in 1 country.

Donald EbeigbeCenter for Neural Engineering, Department of Engineering Science and Mechanics, Pennsylvania State University, University Park, PA, USA.
Tyrus BerryDepartment of Mathematical Sciences, George Mason University, Fairfax, VA, USA.
Michael M NortonCenter for Neural Engineering, Department of Engineering Science and Mechanics, Pennsylvania State University, University Park, PA, USA.
Andrew J WhalenCenter for Neural Engineering, Department of Engineering Science and Mechanics, Pennsylvania State University, University Park, PA, USA.
Dan SimonDepartment of Electrical Engineering and Computer Science, Cleveland State University, Cleveland, OH, USA.
Timothy SauerDepartment of Mathematical Sciences, George Mason University, Fairfax, VA, USA.
Steven J SchiffCenter for Neural Engineering and Center for Infectious Disease Dynamics, Departments of Engineering Science and Mechanics, Neurosurgery, and Physics, Pennsylvania State University, University Park, PA, USA.
Pennsylvania State University · USGeorge Mason University · USCleveland State University · US

Funding

Predictive Personalized Public Health (P3H): A Novel Paradigm to Treat Infectious DiseaseR01AI145057 · NIAID · YALE UNIVERSITY · PI SCHIFF, STEVEN J · 2018 to 2022
$8.1M
NIAID NIH HHS R01 AI145057
6 · The paper itself

Abstract

The unscented transform uses a weighted set of samples called sigma points to propagate the means and covariances of nonlinear transformations of random variables. However, unscented transforms developed using either the Gaussian assumption or a minimum set of sigma points typically fall short when the random variable is not Gaussian distributed and the nonlinearities are substantial. In this paper, we develop the generalized unscented transform (GenUT), which uses 2

Indexed as

EstimationInfectious diseaseKalman filteringProbability distributionsUnscented transform

Identifiers

PMID33850954
PMCPMC8043458
OpenAlexW3145146944

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

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.