Bayesian approach using nonhomogeneous Poisson process is considered for modeling software reliability problems. A generalized gamma and lognormal order statistics models are considered to model epochs of the failures of software. Metropolis algorithms along with Gibbs steps are proposed to perform the Bayesian inference of such models. Some Bayesian model diagnostics are developed and incorpor...

This article provides a Bayesian analysis of the multivariate linear model with polytomous variables. The computational burden due to the intractable multiple integrals induced by the polytomous variables and the model is solved by augmenting the underlying latent continuous measurements of the observed polytomous data. A Gibbs sampler algorithm is implemented to produce the Bayesian estimate. ...

We describe a tolerance interval approach for assessing agreement in method comparison data that may be left censored. We model the data using a mixed model and discuss a Bayesian and a frequentist methodology for inference. A simulation study suggests that the Bayesian approach with noninformative priors provides a good alternative to the frequentist one for moderate sample sizes as the latter...

This paper presents a methodology for real-time estimation of water distribution system state parameters using a dynamic Bayesian network to combine current observations with knowledge of past system behavior. The dynamic Bayesian network presented here allows the flexibility to model both discrete and continuous variables and represent causal relationships that exist within the distribution sy...

We develop a Bayesian uncertainty quantification framework using a local binary tree surrogate model that is able to make use of arbitrary Bayesian regression methods. The tree is adaptively constructed using information about the sensitivity of the response and is biased by the underlying input probability distribution. The local Bayesian regressions are based on a reformulation of the relevan...

The current work introduces a novel combination of two Bayesian tools, Gaussian Processes (GPs), and the use of the Approximate Bayesian Computation (ABC) algorithm for kernel selection and parameter estimation for machine learning applications. The combined methodology that this research article proposes and investigates offers the possibility to use different metrics and summary statistics of...

The maritime industry is currently ongoing into a digital transformation to develop cleaner, safer and smarter transport services. Establishing such services requires identifying assessing new emerging risks as software design flaws. Thus, suitable hazard identification risk analysis methods must be developed implemented for these complex This study aims novel methodology by integrating Systems...