Bayesian Process Capability Analysis

Bayesian Process Capability Analysis is a statistical methodology within process capability analysis that incorporates prior knowledge and observed data through Bayesian inference to estimate and assess the capability of a process, distinguishing itself from classical frequentist approaches by providing probabilistic interpretations of capability indices under uncertainty.

11

Publications

330

Citations

18

Authors

8

Institutions

Publications per year

2017–2026

0

Authors

18

  1. CW
    Chien‐Wei Wu

    National Tsing Hua University, Taiwan

    4 publications · 90 citations

    4

    h-index

  2. WL
    W. L. Pearn

    National Yang Ming Chiao Tung University, Taiwan

    3 publications · 81 citations

    3

    h-index

  3. SW
    Smiley W. Cheng

    University of Manitoba, Canada

    1 publications · 60 citations

    1

    h-index

  4. HK
    Hsin-Lin Kuo

    1 publications · 18 citations

    1

    h-index

  5. TI
    Telba Irony

    United States Food and Drug Administration, United States

    1 publications · 37 citations

    1

    h-index

1–5 of 18

Venues

  1. 2

    h-index

  2. 1

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  3. IIE Transactions

    1 publications · 60 citations

    1

    h-index

  4. European Journal of Operational Research

    1 publications · 42 citations

    1

    h-index

  5. Journal of Probability and Statistics

    JPS · 1 publications · 22 citations

    1

    h-index

1–5