Search Results for author:
Found 4 papers, 1 papers with code
Model orthogonalization and Bayesian forecast mixing via Principal Component Analysis
One can improve predictability in the unknown domain by combining forecasts of imperfect complex computational models using a Bayesian statistical machine learning framework.
Local Bayesian Dirichlet mixing of imperfect models
To improve the predictability of complex computational models in the experimentally-unknown domains, we propose a Bayesian statistical machine learning framework utilizing the Dirichlet distribution that combines results of several imperfect models.
Variational Inference with Vine Copulas: An efficient Approach for Bayesian Computer Model Calibration
With the advancements of computer architectures, the use of computational models proliferates to solve complex problems in many scientific applications such as nuclear physics and climate research.
Statistical aspects of nuclear mass models
We study the information content of nuclear masses from the perspective of global models of nuclear binding energies.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
