Integrate weighted dependence and skewness based multiblock principal component analysis with Bayesian inference for large-scale process monitoring

Research article (Journal of the Taiwan Institute of Chemical Engineers, 2021) · cited 13× · AI/ML
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Integrate weighted dependence and skewness based multiblock principal component analysis with Bayesian inference for large-scale process monitoring

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Integrate weighted dependence and skewness based multiblock principal component analysis with Bayesian inference for large-scale process monitoring is a scholarly article[1].

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APA 4ort.xyz Knowledge Graph. (2026). Integrate weighted dependence and skewness based multiblock principal component analysis with Bayesian inference for large-scale process monitoring. Retrieved May 24, 2026, from https://4ort.xyz/entity/integrate-weighted-dependence-and-skewness-based-multiblock-principal-component-analysis-with-bayesian-inference-for-lar
MLA “Integrate weighted dependence and skewness based multiblock principal component analysis with Bayesian inference for large-scale process monitoring.” 4ort.xyz Knowledge Graph, 4ort.xyz, 24 May. 2026, https://4ort.xyz/entity/integrate-weighted-dependence-and-skewness-based-multiblock-principal-component-analysis-with-bayesian-inference-for-lar.
BibTeX @misc{4ortxyz_integrate-weighted-dependence-and-skewness-based-multiblock-principal-component-analysis-with-bayesian-inference-for-lar_2026, author = {{4ort.xyz Knowledge Graph}}, title = {{Integrate weighted dependence and skewness based multiblock principal component analysis with Bayesian inference for large-scale process monitoring}}, year = {2026}, url = {https://4ort.xyz/entity/integrate-weighted-dependence-and-skewness-based-multiblock-principal-component-analysis-with-bayesian-inference-for-lar}, note = {Accessed: 2026-05-24}}
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