• Corpus ID: 239050340

Nuclear data evaluation with Bayesian networks

@inproceedings{Schnabel2021NuclearDE,
  title={Nuclear data evaluation with Bayesian networks},
  author={Georg Schnabel and Roberto Capote and Arjan J. Koning and David Brown},
  year={2021}
}
Bayesian networks are graphical models to represent the deterministic and probabilistic relationships between variables within the Bayesian framework. The knowledge of all variables can be updated using new information about some of the variables. The Bayesian Generalized Linear Least Squares method can be regarded as an inference method for Bayesian networks of variables with multivariate normal priors and linear relationships between them. We show that relying explicitly on the Bayesian… 

References

SHOWING 1-10 OF 172 REFERENCES
Probabilistic Graphical Models - Principles and Techniques
TLDR
The framework of probabilistic graphical models, presented in this book, provides a general approach for causal reasoning and decision making under uncertainty, allowing interpretable models to be constructed and then manipulated by reasoning algorithms.
Large errors and severe conditions
Abstract Physical parameters that can assume real-number values over a continuous range are generally represented by inherently positive random variables. However, if the uncertainties in these
Gaussian Processes for Machine Learning
TLDR
The treatment is comprehensive and self-contained, targeted at researchers and students in machine learning and applied statistics, and deals with the supervised learning problem for both regression and classification.
Large scale Bayesian nuclear data evaluation with consistent model defects
The aim of nuclear data evaluation is the reliable determination of cross sections and related observables of the atomic nuclei. To this end, evaluation methods are applied which combine the
Bayesian Probability Theory: Applications in the Physical Sciences
TLDR
This book presents the roots, applications and numerical implementation of probability theory, and covers advanced topics such as maximum entropy distributions, stochastic processes, parameter estimation, model selection, hypothesis testing and experimental design.
Updated Users' Guide for SAMMY Multilevel R-matrix Fits to Neutron Data Using Bayes' Equation
In 1980 the multilevel multichannel R-matrix code SAMMY was released for use in analysis of neutron-induced cross section data at the Oak Ridge Electron Linear Accelerator. Since that time, SAMMY has
Probabilistic reasoning in intelligent systems - networks of plausible inference
  • J. Pearl
  • Computer Science
    Morgan Kaufmann series in representation and reasoning
  • 1989
TLDR
The author provides a coherent explication of probability as a language for reasoning with partial belief and offers a unifying perspective on other AI approaches to uncertainty, such as the Dempster-Shafer formalism, truth maintenance systems, and nonmonotonic logic.
Probability
TLDR
This course can be used as a preparation for the first (Probability) actuarial exam and the central limit theorem and classical sampling distributions.
Interpolation of Spatial Data: Some Theory for Kriging
  • R. Woodard
  • Computer Science, Mathematics
    Technometrics
  • 2000
TLDR
This chapter discusses the role of asymptotics for BLPs, and applications of equivalence and orthogonality of Gaussian measures to linear prediction, and the importance of Observations not part of a sequence.
Nuclear Data Sheets for A = 163
Abstract Nuclear structure data for all nuclei with mass number A=163 have been evaluated. Adopted values for level properties and detailed level and decay schemes are presented for each nucleus,
...
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2
3
4
5
...