Fig 1.
Effect of hyperparameters on the prior distribution.
Fig 2.
Effect of hyperparameters on posterior distribution.
Table 1.
Bayes estimators and posterior risks under different loss functions.
Fig 3.
Effect of hyperparameters on the (a) BE and (b) PR under SELF.
Fig 4.
Effect of hyperparameters on the (a) BE and (b) PR under WSELF.
Fig 5.
Effect of hyperparameters on the (a) BE and (b) PR under MSELF.
Fig 6.
Effect of hyperparameters on the (a) BE and (b) PR under KLF.
Fig 7.
Effect of hyperparameters on the (a) BE and (b) PR under DLF.
Fig 8.
Effect of hyperparameters on the (a) BE and (b) PR under PLF.
Fig 9.
ARL1 under classical and Bayesian setups for n = 5 at λ = 0.1.
Fig 10.
ARL1 under Classical and Bayesian setups for n = 10 at λ = 0.1.
Fig 11.
ARL1 under classical and Bayesian setups for n = 15 at λ = 0.1.
Fig 12.
ARL1 under classical and Bayesian setups for n = 5 at λ = 0.2.
Fig 13.
ARL1 under classical and Bayesian setups for n = 10 at λ = 0.2.
Fig 14.
ARL1 under classical and Bayesian setups for n = 15 at λ = 0.
Table 2.
ARL, SDRL and MDRL comparison using frequentist and Bayesian setups under different loss functions for n = 5, λ = 0.1 at ARL0 = 370.
Table 3.
ARL, SDRL and MDRL comparison using frequentist and Bayesian setups under different loss functions for n = 10, λ = 0.1 at ARL0 = 370.
Table 4.
ARL, SDRL and MDRL comparison using frequentist and Bayesian setups under different loss functions for n = 15, λ = 0.1 at ARL0 = 370.
Table 5.
ARL, SDRL and MDRL comparison using frequentist and Bayesian setups under different loss functions for n = 5, λ = 0.2 at ARL0 = 370.
Table 6.
ARL, SDRL and MDRL comparison using frequentist and Bayesian setups under different loss functions for n = 10, λ = 0.2 at ARL0 = 370.
Table 7.
ARL, SDRL and MDRL comparison using frequentist and Bayesian setups under different loss functions for n = 15, λ = 0.2 at ARL0 = 370.
Fig 15.
Classical process monitoring using simulated data for n = 5.
Fig 16.
Bayesian process monitoring for all the loss functions using simulated data for n = 5.
Table 8.
Model selection criteria for IGD and weibull models.
Fig 17.
Classical process monitoring using the manufacturing data from the aerospace industry.
Fig 18.
Bayesian process monitoring for all the loss functions using manufacturing data from the aerospace industry.