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INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT International Peer Reviewed & Refereed Journals, Open Access Journal ISSN Approved Journal No: 2456-4184 | Impact factor: 8.76 | ESTD Year: 2016 Scholarly open access journals, Peer-reviewed, and Refereed Journals, Impact factor 8.76 (Calculate by google scholar and Semantic Scholar | AI-Powered Research Tool) , Multidisciplinary, Monthly, Indexing in all major database & Metadata, Citation Generator, Digital Object Identifier(DOI) |
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| Paper Title: | BAYESIAN INFERENCE METHODS FOR UNCERTAINTY QUANTIFICATION IN DATA SCIENCE: TAMING THE COMPLEXITY |
| Authors Name: | Dr. Abdul Khadeer , Mrs. A. Supriya |
| Download E-Certificate: | Download |
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IJNRD_216780
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| Published Paper Id: | IJNRD2403577 |
| Published In: | Volume 9 Issue 3, March-2024 |
| DOI: | |
| Abstract: | In the realm of data science, the accurate quantification of uncertainty is paramount for informed decision-making and robust predictive modeling. Bayesian inference offers a principled framework for addressing uncertainty by providing a systematic approach to update beliefs in the presence of data. This paper presents an overview of Bayesian inference methods tailored for uncertainty quantification in data science applications, focusing on strategies to tame the complexity inherent in real-world datasets. To explore recent advancements in Bayesian deep learning, which merge the strengths of probabilistic modeling with the expressive power of deep neural networks. The importance of model validation and diagnostics in Bayesian inference, emphasizing the need for assessing model adequacy and identifying potential. This paper underscores the significance of Bayesian inference methods for uncertainty quantification in data science. By leveraging Bayesian techniques, practitioners can enhance the reliability, interpretability, and generalization capabilities of their models, ultimately advancing the field of data science towards more principled and dependable analyses. |
| Keywords: | Bayesian inference, Data science, Complexity, Variational inference, Bayesian deep learning, Healthcare, Environmental science |
| Cite Article: | "BAYESIAN INFERENCE METHODS FOR UNCERTAINTY QUANTIFICATION IN DATA SCIENCE: TAMING THE COMPLEXITY", International Journal of Novel Research and Development (www.ijnrd.org), ISSN:2456-4184, Vol.9, Issue 3, page no.f674-f679, March-2024, Available :http://www.ijnrd.org/papers/IJNRD2403577.pdf |
| Downloads: | 00037 |
| ISSN: |
2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar| ESTD YEAR: 2016 An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.76 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator |
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Published Paper ID:IJNRD2403577 Registration ID: 216780 Published In: Volume 9 Issue 3, March-2024 DOI (Digital Object Identifier): Page No: f674-f679 Country: -, -, India Research Area: Engineering Publisher : IJ Publication Published Paper URL : https://www.ijnrd.org/viewpaperforall?paper=IJNRD2403577 Published Paper PDF: https://www.ijnrd.org/papers/IJNRD2403577 |
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ISSN: 2456-4184
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