Bayesian Anomaly Detection
Bayesian Anomaly Detection is a probabilistic framework for identifying anomalous observations in time series data by modeling uncertainty through Bayesian inference, distinguishing itself from other anomaly detection methods through its principled incorporation of prior knowledge and posterior updating to quantify the probability of anomalies.
191
Publications
11.9K
Citations
563
Authors
287
Institutions
Publications per year
2017–2026
83
191
Data analysis: a Bayesian tutorial
Choice Reviews Online · 1997 · 1.7K citations
Data Analysis: A Bayesian Tutorial
Eric P. Fox, D. S. Sivia · Technometrics · 1998 · 481 citations
Longhui Wei, Shiliang Zhang, Hantao Yao et al. · 2017 · 424 citations · Full text
Deep and Confident Prediction for Time Series at Uber
Lingxue Zhu, Nikolay Laptev · 2017 · 351 citations · Full text
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University of Illinois Urbana-Champaign
Urbana, United States
5 h-index
10 publications
570 citations
University of Southern California
Los Angeles, United States
5 h-index
10 publications
610 citations
University of Cambridge
Cambridge, United Kingdom
5 h-index
12 publications
382 citations
Carnegie Mellon University
Pittsburgh, United States
4 h-index
8 publications
587 citations
University of Connecticut
Storrs, United States
3 h-index
4 publications
150 citations
Tsinghua University
Beijing, China
3 h-index
11 publications
646 citations
North Atlantic Treaty Organization
Brussels, Belgium
3 h-index
9 publications
153 citations
University of Maryland, College Park
College Park, United States
3 h-index
7 publications
351 citations
1–8 of 287
Repository · 6 publications · 144 citations
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4 publications · 96 citations
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3 publications · 361 citations
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3 publications · 108 citations
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3 publications · 98 citations
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