Changes from Classical Statistics to Modern Statistics and Data Science
में बचाया:
| में प्रकाशित: | arXiv.org (Oct 30, 2022), p. n/a |
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| मुख्य लेखक: | |
| अन्य लेखक: | , |
| प्रकाशित: |
Cornell University Library, arXiv.org
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| विषय: | |
| ऑनलाइन पहुंच: | Citation/Abstract Full text outside of ProQuest |
| टैग: |
कोई टैग नहीं, इस रिकॉर्ड को टैग करने वाले पहले व्यक्ति बनें!
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| 001 | 2733855355 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 2331-8422 | ||
| 035 | |a 2733855355 | ||
| 045 | 0 | |b d20221030 | |
| 100 | 1 | |a Zhang, Kai | |
| 245 | 1 | |a Changes from Classical Statistics to Modern Statistics and Data Science | |
| 260 | |b Cornell University Library, arXiv.org |c Oct 30, 2022 | ||
| 513 | |a Working Paper | ||
| 520 | 3 | |a A coordinate system is a foundation for every quantitative science, engineering, and medicine. Classical physics and statistics are based on the Cartesian coordinate system. The classical probability and hypothesis testing theory can only be applied to Euclidean data. However, modern data in the real world are from natural language processing, mathematical formulas, social networks, transportation and sensor networks, computer visions, automations, and biomedical measurements. The Euclidean assumption is not appropriate for non Euclidean data. This perspective addresses the urgent need to overcome those fundamental limitations and encourages extensions of classical probability theory and hypothesis testing , diffusion models and stochastic differential equations from Euclidean space to non Euclidean space. Artificial intelligence such as natural language processing, computer vision, graphical neural networks, manifold regression and inference theory, manifold learning, graph neural networks, compositional diffusion models for automatically compositional generations of concepts and demystifying machine learning systems, has been rapidly developed. Differential manifold theory is the mathematic foundations of deep learning and data science as well. We urgently need to shift the paradigm for data analysis from the classical Euclidean data analysis to both Euclidean and non Euclidean data analysis and develop more and more innovative methods for describing, estimating and inferring non Euclidean geometries of modern real datasets. A general framework for integrated analysis of both Euclidean and non Euclidean data, composite AI, decision intelligence and edge AI provide powerful innovative ideas and strategies for fundamentally advancing AI. We are expected to marry statistics with AI, develop a unified theory of modern statistics and drive next generation of AI and data science. | |
| 653 | |a Hypothesis testing | ||
| 653 | |a Social networks | ||
| 653 | |a Data analysis | ||
| 653 | |a Data science | ||
| 653 | |a Computer vision | ||
| 653 | |a Euclidean space | ||
| 653 | |a Machine learning | ||
| 653 | |a Diffusion | ||
| 653 | |a Statistical analysis | ||
| 653 | |a Decision analysis | ||
| 653 | |a Artificial intelligence | ||
| 653 | |a Hypotheses | ||
| 653 | |a Neural networks | ||
| 653 | |a Probability theory | ||
| 653 | |a Manifolds (mathematics) | ||
| 653 | |a Natural language processing | ||
| 653 | |a Differential equations | ||
| 653 | |a Deep learning | ||
| 653 | |a Euclidean geometry | ||
| 653 | |a Cartesian coordinates | ||
| 653 | |a Graph neural networks | ||
| 700 | 1 | |a Liu, Shan | |
| 700 | 1 | |a Xiong, Momiao | |
| 773 | 0 | |t arXiv.org |g (Oct 30, 2022), p. n/a | |
| 786 | 0 | |d ProQuest |t Engineering Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/2733855355/abstract/embedded/ZKJTFFSVAI7CB62C?source=fedsrch |
| 856 | 4 | 0 | |3 Full text outside of ProQuest |u http://arxiv.org/abs/2211.03756 |