An adaptively weighted stochastic gradient MCMC algorithm for Monte Carlo simulation and global optimization is a research paper published in Statistics and Computing (2022). On theSindex it has a DataRank of 0. It has been cited 9 times.
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National Science Foundation
Grant: DMS-2015498
National Institutes of Health
Grant: R01-GM126089
National Science Foundation
Grant: DMS-2053746
U.S. Department of Energy
Grant: DE-SC0021142
Brookhaven National Laboratory
Grant: subcontract 382247
National Institutes of Health
Grant: R01-GM117597
National Science Foundation
Grant: 2053746
Collaborative Research: Inference and Uncertainty Quantification for High Dimensional Systems in Remote Sensing: Methods, Computation, and Applications
National Science Foundation
Grant: 2015498
Scalable Algorithms for Bayesian On-Line Learning with Large-Scale Dynamic Data
National Science Foundation
Grant: 1736364
Collaborative Research: AMPS: Multi-Fidelity Modeling via Machine Learning for Real-time Prediction of Power System Behavior
National Science Foundation
Grant: 1555072
CAREER: Uncertainty Quantification and Big Data Analysis in Interconnected Systems: Algorithms, Computations, and Applications
National Institutes of Health
Grant: 5R01GM126089-03
An Imputation-Consistency Algorithm for Biomedical Complex Data Analysis
National Institutes of Health
Grant: 1R01GM117597-01
Equivalent Partial Correlation Methods for Integrative Genetic Network Analysis
FWCI
1.55
Citation Percentile
0.8%
Citation Trend
Fields of Study
Keywords
Sustainable Development Goals