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

Authors

275

  1. MR
    Muhammad Riaz

    King Fahd University of Petroleum and Minerals, Saudi Arabia

    6 publications · 128 citations

    6

    h-index

  2. MN
    Muhammad Noor‐ul‐Amin

    COMSATS University Islamabad, Pakistan

    5 publications · 130 citations

    5

    h-index

  3. SA
    Sajid Ali

    Quaid-i-Azam University, Pakistan

    4 publications · 73 citations

    4

    h-index

  4. BH
    Biao Huang

    University of Alberta, Canada

    4 publications · 262 citations

    4

    h-index

  5. FW
    Fuli Wang

    Northeastern University, China

    3 publications · 74 citations

    3

    h-index

1–5 of 275

Venues

  1. 10

    h-index

  2. International Journal of Production Research

    7 publications · 283 citations

    7

    h-index

  3. Technometrics

    6 publications · 186 citations

    6

    h-index

  4. IIE Transactions

    5 publications · 127 citations

    5

    h-index

  5. European Journal of Operational Research

    4 publications · 156 citations

    4

    h-index

1–5