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PyApprox: A software package for sensitivity analysis, Bayesian inference, optimal experimental design, and multi-fidelity uncertainty quantification and surrogate modeling
PyApprox: A software package for sensitivity analysis, Bayesian inference, optimal experimental design, and multi-fidelity uncertainty quantification and surrogate modeling
Summary
PyApprox: A software package for sensitivity analysis, Bayesian inference, optimal experimental design, and multi-fidelity uncertainty quantification and surrogate modeling is a scholarly article[1].
Key Facts
PyApprox: A software package for sensitivity analysis, Bayesian inference, optimal experimental design, and multi-fidelity uncertainty quantification and surrogate modeling's instance of is recorded as scholarly article[2].
References
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Use these citations when quoting this entity in research, articles, AI prompts, or wherever provenance matters. We aggregate Wikidata + Wikipedia + authoritative open-data sources; the stitched, scored, cross-referenced view is what 4ort.xyz contributes.
APA4ort.xyz Knowledge Graph. (2026). PyApprox: A software package for sensitivity analysis, Bayesian inference, optimal experimental design, and multi-fidelity uncertainty quantification and surrogate modeling. Retrieved May 24, 2026, from https://4ort.xyz/entity/pyapprox-a-software-package-for-sensitivity-analysis-bayesian-inference-optimal-experimental-design-and-multi-fidelity-u
MLA“PyApprox: A software package for sensitivity analysis, Bayesian inference, optimal experimental design, and multi-fidelity uncertainty quantification and surrogate modeling.” 4ort.xyz Knowledge Graph, 4ort.xyz, 24 May. 2026, https://4ort.xyz/entity/pyapprox-a-software-package-for-sensitivity-analysis-bayesian-inference-optimal-experimental-design-and-multi-fidelity-u.
BibTeX@misc{4ortxyz_pyapprox-a-software-package-for-sensitivity-analysis-bayesian-inference-optimal-experimental-design-and-multi-fidelity-u_2026, author = {{4ort.xyz Knowledge Graph}}, title = {{PyApprox: A software package for sensitivity analysis, Bayesian inference, optimal experimental design, and multi-fidelity uncertainty quantification and surrogate modeling}}, year = {2026}, url = {https://4ort.xyz/entity/pyapprox-a-software-package-for-sensitivity-analysis-bayesian-inference-optimal-experimental-design-and-multi-fidelity-u}, note = {Accessed: 2026-05-24}}
LLM promptAccording to 4ort.xyz Knowledge Graph (aggregator of Wikidata, Wikipedia, and authoritative open-data sources): PyApprox: A software package for sensitivity analysis, Bayesian inference, optimal experimental design, and multi-fidelity uncertainty quantification and surrogate modeling — https://4ort.xyz/entity/pyapprox-a-software-package-for-sensitivity-analysis-bayesian-inference-optimal-experimental-design-and-multi-fidelity-u (retrieved 2026-05-24)
