Computationally efficient Bayesian sequential function monitoring

Wright Shamp, Roumen Varbanov, Eric Chicken, Antonio Linero, Yun Yang

Research output: Contribution to journalArticlepeer-review


In functional sequential process monitoring, a process is characterized by sequences of observations called profiles which are monitored over time for stability. The goal is to halt a process when the process generating these observations deviates from a specified in control standard. We propose a Bayesian sequential process control (SPC) methodology which uses wavelets to monitor the functional responses and detect out of control profiles. Our contribution is to propose a solution to the growing computational cost by constructing an efficient and accurate approximation to the posterior distribution of the wavelet coefficients, without recourse to Markov chain Monte Carlo.

Original languageEnglish (US)
Pages (from-to)1-19
Number of pages19
JournalJournal of Quality Technology
Issue number1
StatePublished - 2021


  • Bayesian
  • Phase II
  • profile monitoring
  • statistical process control
  • wavelets

ASJC Scopus subject areas

  • Safety, Risk, Reliability and Quality
  • Strategy and Management
  • Management Science and Operations Research
  • Industrial and Manufacturing Engineering


Dive into the research topics of 'Computationally efficient Bayesian sequential function monitoring'. Together they form a unique fingerprint.

Cite this