Bayesian Statistical Process Control
Bayesian Statistical Process Control is a statistical process control methodology that incorporates Bayesian inference to sequentially update the probability distribution of process parameters, thereby enabling real-time monitoring and decision-making under uncertainty by integrating prior knowledge with observed data.
124
Publications
4.1K
Citations
275
Authors
163
Institutions
Publications per year
2017–2026
40
Variants
Bspc
Parents
124
1–5 of 124
275
King Fahd University of Petroleum and Minerals, Saudi Arabia
6 publications · 128 citations
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5
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4
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4
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3
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1–5 of 275
163
COMSATS University Islamabad
Islamabad, Pakistan
7 h-index
10 publications
283 citations

King Fahd University of Petroleum and Minerals
Dhahran, Saudi Arabia
6 h-index
6 publications
128 citations
Quaid-i-Azam University
Islamabad, Pakistan
6 h-index
9 publications
172 citations

University of Toronto
Toronto, Canada
5 h-index
6 publications
223 citations
Aristotle University of Thessaloniki
Thessaloniki, Greece
4 h-index
6 publications
320 citations

Pennsylvania State University
State College, United States
4 h-index
5 publications
174 citations

Hong Kong University of Science and Technology
Hong Kong, Hong Kong
4 h-index
7 publications
200 citations

Northeastern University
Shenyang, China
4 h-index
10 publications
239 citations
1–8 of 163
10 publications · 223 citations
10
h-index
7 publications · 283 citations
7
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6 publications · 186 citations
6
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5 publications · 127 citations
5
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4 publications · 156 citations
4
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1–5