The transition from hybrid covariances to fully ensemble-derived background-error covariances for Numerical Weather Prediction (NWP) at Environment and Climate Change Canada (ECCC) is explored in this paper. It discusses the evolution of covariance formulations, the use of scale-dependent localizati

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Multinomial Distribution is a powerful tool used in statistical analysis to model outcomes of events with multiple categories. This distribution is applied to scenarios where each trial has several possible outcomes, and the sum of probabilities of all outcomes is equal to 1. By defining random vari

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In this collection by Tim Johnson, Libby McCutchan, and Alejandro Sonzogni from the National Nuclear Data Center, various aspects of nuclear data needs and beta-delayed neutron covariances are discussed. Topics include different radiation types, gamma decay heat, beta transitions, antineutrinos, and

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This study by A.A. Sonzogni and E.A. McCutchan focuses on developing cumulative fission yield covariances for uncertainty quantification in nuclear reactors. The research involves calculating cumulative fission yields, using decay data and nuclear databases, to improve accuracy in predicting fission

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This workshop, held at the Elliott School of International Affairs, discussed the impact of unrealistic or missing cross-section covariances in nuclear data activities. The need for complete nuclear data covariances, methods for credibility evaluation, and challenges with missing and questionable co

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Comprehensive analysis and visualization of beta-delayed neutron covariances, fission yields, and their implications in nuclear physics applications. The research covers calculations of delayed nu-bar, neutron probabilities, and recommendations for various systems. Disagreements in fission yields fo

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In the realm of high-dimensional classification with scarce positive examples, challenges like imbalanced data distribution and limited data availability can hinder traditional classification methods. This study explores innovative strategies such as robust covariances and smoothed kernel distributi

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Latent Variable Modeling, including Factor Analysis and Path Analysis, plays a crucial role in statistical analysis to uncover hidden relationships and causal effects among observed variables. This method involves exploring covariances, partitioning variances, and estimating causal versus non-causal

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