№ files_lp_3_process_7_031667
File format: docx
Character count: 1491
File size: 16 KB
Year:
2023
Region / City:
East Lansing, Michigan
Subject:
Nuclear Physics, Bayesian Methods
Document Type:
Research Paper
Organization / Institution:
Facility for Rare Isotope Beams, Michigan State University, Department of Physics and Astronomy, Michigan State University
Authors:
Knight, Bailey, Lalit, Sudhanva, Godbey, Kyle, Giuliani, Pablo, Nazarewicz, Witold
Target Audience:
Nuclear physicists, researchers in astrophysics
Period of Effectiveness:
N/A
Approval Date:
N/A
Modification Date:
N/A
Contextual Description:
A research paper exploring the use of heteroscedastic uncertainties in Bayesian nuclear model combination, aiming to enhance predictive performance in nuclear physics models.
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Year:
2026
Region / City:
Warrington, United Kingdom / Tokyo, Japan
Topic:
Radioactive Waste Management, Bayesian Statistics, Waste Classification
Document Type:
Research Paper
Institution:
National Nuclear Laboratory, Japan Atomic Energy Agency
Author:
P. Hiller, C. Pyke, K. Yoshikazu, O. Keiichi
Target Audience:
Researchers, Nuclear Industry Professionals
Period of Validity:
Ongoing
Approval Date:
Not specified
Date of Changes:
Not specified
Year:
2023
Region / City:
N/A
Topic:
Drug repurposing, machine learning models
Document Type:
Research article
Organization / Institution:
N/A
Author:
N/A
Target Audience:
Researchers, Data Scientists
Effective Period:
N/A
Approval Date:
N/A
Modification Date:
N/A
Description of Document:
A research article comparing the performance of different machine learning models in predicting drug-disease approval likelihood using various optimization techniques and cross-validation methods.
Year:
2026
Region / City:
Not specified
Topic:
Lung cancer morphology and survival analysis
Document Type:
Research Supplement
Organization / Institution:
Not specified
Author:
Not specified
Target Audience:
Researchers, oncologists
Period of Action:
Not specified
Approval Date:
Not specified
Date of Changes:
Not specified
Year:
2020
Region / city:
N/A
Topic:
Statistical Modeling
Document Type:
Research Supplement
Organization / Institution:
N/A
Author:
N/A
Target Audience:
Researchers in Statistical Modeling and Fisheries Science
Period of Validity:
N/A
Approval Date:
N/A
Modification Date:
N/A
Methodology:
Bayesian Mixture Model
Modeling Software:
JAGS, R
Code:
Provided
Note:
Context
Year:
2022
Author:
Dr. James Theimer
Organization:
Homeland Security Community of Best Practices
Distribution:
Approved for public release; distribution unlimited
Contract:
FA8075-18-D-0002, Task FA8075-21-F-0074
Location:
Wright-Patterson Air Force Base, Ohio, USA
Version:
1, FY22
Keywords:
Bayesian statistics, Model checking, Model assessment
Case Number:
88ABW-2022-0916
Publication Date:
30 November 2022
Intended Audience:
DHS workforce, program managers, T&E practitioners
Content Type:
Technical report
Method:
Box model-checking criterion
Year:
2021
Region / City:
Visakhapatnam, India
Topic:
Channel Estimation, Massive MIMO, Pilot Contamination
Document Type:
Research Paper
Organization / Institution:
Sanketika Vidya Parishad Engineering College
Author(s):
M. Keerthi, T. Ravi Babu, Manas Ranjan Biswal
Target Audience:
Researchers, Engineers, Academics in Communications
Period of Effectiveness:
Ongoing
Date of Approval:
Not specified
Date of Changes:
Not specified
Year:
2015
Region / City:
N/A
Subject:
Bayesian Model Averaging, Structural Equation Modeling
Document Type:
Package Documentation
Organization / Institution:
N/A
Author:
Chansoon Lee, David Kaplan
Target Audience:
Researchers, Data Scientists, Statisticians
Validity Period:
N/A
Approval Date:
N/A
Date of Changes:
N/A
URL:
http://bise.wceruw.org/publications.html
References:
Kaplan, D., Lee, C. (2015). Bayesian Model Averaging Over Directed Acyclic Graphs With Implications for the Predictive Performance of Structural Equation Models. Structural Equation Modeling.
Description:
Bayesian Model Averaging (BMA) applied to Structural Equation Modeling, expanding upon previous work by Madigan et al. and Raftery et al. with a focus on directed acyclic graphs.
Year:
2023
Region / City:
Suzhou, China
Topic:
Kawasaki Disease, Retreatment Strategies
Document Type:
Research Study
Authors:
Jiyu Li, Xichen Zhong, Ye Chen, Yunjia Tang, Qiuqin Xu, Guanghui Qian, Ying Liu, Shuhui Wang, Haitao Lv, Xuan Li
Target Audience:
Researchers, Healthcare Professionals, Cardiologists
Period of Validity:
Not specified
Approval Date:
Not specified
Date of Changes:
Not specified
Year:
2026
Region:
International
Subject:
Bayesian statistical modeling, age estimation
Document type:
Code repository / Technical documentation
Institution:
Independent research compilation
Author:
Not specified
Intended audience:
Statisticians, data analysts, demographers
Programming language:
R
Number of functions:
6
Methods included:
GompertzFit, Survivorship, TA, HPDR, MHPDRCI, MHPDRMR
Data requirements:
Age-at-death datasets, age indicator datasets
Dependencies:
survival, optimx, VGAM, nleqslv R packages
License:
Not specified