Kernel density estimation

Ebook "Energy risk modeling: Applied modeling methods for risk managers" is a primer on statistical methods for managers, students and anybody interested in the field. Illustrated through elementary and more advanced statistical Methods, it is primarily aimed at those individuals who need a gentle introduction in how to go about using statistical methods for modeling energy price risk. Statistical ideas are presented by outlining the necessary concepts and illustrating how these ideas can be implemented.

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Ebook "Statistical pattern recognition" includes content: Introduction to statistical pattern recognition, density estimation – parametric, density estimation – nonparametric, linear discriminant analysis, nonlinear discriminant analysis – kernel methods, nonlinear discriminant analysis – projection methods, tree-based methods, performance, feature selection and extraction, clustering, additional topics,... and other contents.

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In addition, we present modifications of two well-known algorithms (greedy hill-climbing and PC) to learn the structure of a semiparametric Bayesian network from data. To realize this, we employ a score function based on cross-validation.

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In this study, evaluate anomaly detection efficiency on UAV images through a number of common kernel functions typically cited in researches on KDE, and follow up with making recommendations for appropriate uses of kernel functions. Experiments and evaluations are carried out on the sample data set photographed on varied terrain types and objects of interest.

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DNA methylation patterns store epigenetic information in the vast majority of eukaryotic species. The relatively high costs and technical challenges associated with the detection of DNA methylation however have created a bias in the number of methylation studies towards model organisms.

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The performances of different types of banks may vary due to heterogeneous technology, which can be examined by metafrontier analysis. However, the metafrontier constructed in most existing literature is concave, resulting in a biased estimation of efficiency.

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This study proposes to conduct uncertainty analysis of CDR for core damage assessment. First, based on source term estimation, the Monte Carlo (MC) and point-kernel integration methods were used to estimate the probability density function of the CDR under different extents of core damage in accident scenarios with late containment failure. Second, the results were verified by comparing the results of both methods.

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Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: Research Article Analytical Plug-In Method for Kernel Density Estimator Applied to Genetic Neutrality Study

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