https://hal.archives-ouvertes.fr/hal-00546144/document

Overview on Bayesian networks applications for dependability, risk analysis and maintenance areas

Safety analysis in process facilities: Comparison of fault tree and Bayesian network approaches

https://link.springer.com/content/pdf/10.1057%2Fjors.1996.159.pdf

Modeling and Analysis of Stochastic Systems

Survival Analysis: Techniques for Censored and Truncated Data

The objective of this work is to determine and study, analyze and elaborate, classify and categorize the main risk analysis and risk-assessment methods and techniques by reviewing the scientific literature. The paper consists of two parts: a) the investigation, presentation and elaboration of the main risk-assessment methodologies and b) the statistical analysis, classification, and comparative study of the corresponding scientific papers published by six representative scientific journals of Elsevier B.V. covering the decade 2000–2009. The scientific literature reviewing showed that the risk analysis and assessment techniques are classified into three main categories: (a) the qualitative, (b) the quantitative, and (c) the hybrid techniques (qualitative–quantitative, semi-quantitative). The qualitative techniques are based both on analytical estimation processes, and on the safety managers–engineers ability. According to quantitative techniques, the risk can be considered as a quantity, which can be estimated and expressed by a mathematical relation, under the help of real accidents’ data recorded in a work site. The hybrid techniques, present a great complexity due to their ad hoc character that prevents a wide spreading. The statistical analysis shows that the quantitative methods present the highest relative frequency (65.63%) while the qualitative a lower one (27.68%). Furthermore the hybrid methods remain constantly at a very low level (6.70%) during the entire processing period.

http://risktr.com/dokumanlar/risk%20analysis%20and%20assessment%20methodologies%20in%20th%20work%20sites.pdf

Risk analysis and assessment methodologies in the work sites: On a review, classification and comparative study of the scientific literature of the period 2000–2009

Bayesian networks for multilevel system reliability

Preface.1. Multistate Models and Flowgraph Models.2. Flowgraph Models.3. Inversion of Flowgraph Moment Generating Functions.4. Censored Data Histograms.5. Bayesian Prediction for Flowgraph Models.6. Computation Implementation of Flowgraph Models.7. Semi-Markov Processes.8. Incomplete Data.9. Flowgraph Models for Queuing Systems.Appendix: Moment Generating Functions.References.Author Index.Subject Index.

Flowgraph Models for Multistate Time-to-Event Data

We present methodology giving highly accurate approximations for Bayesian predictive densities and distribution functions of first passage times between states of a semi-Markov process with a finite number of states. When the states describe a degenerative disorder with an absorbing end state, such predictive distributions are the survival distributions of a patient. We illustrate these methods with a variety of examples, including data from the San Francisco AIDS study. We achieve our approximations using a three-step sequence. First, we introduce advanced concepts of flowgraph theory, which allow us to compute the moment generating function of the first passage time given the model parameters. Next, we use saddlepoint approximations to convert this into a density or distribution function conditional on the model parameter. Finally, we use Monte Carlo methods to remove dependence on the model parameter. These methods apply quite generally to all finite-state semi-Markov models in discrete or con...

Stochastic Network Models for Survival Analysis

Perusal of quality- and reliability-engineering literature indicates some confusion over the meaning of accelerated life testing (ALT), highly accelerated life testing (HALT), highly accelerated stress screening (HASS), and highly accelerated stress auditing (HASA). In addition, there is a significant conflict between testing as part of an iterative process of finding and removing defects and testing as a means of estimating or predicting product reliability. We review the basics of these testing methods and describe how they relate to statistical methods for estimation and prediction of reliability and reliability growth. We also outline potential synergies to help reconcile statistical and engineering approaches to accelerated testing, resulting in better product quality at lower cost.

Accelerated Test Methods for Reliability Prediction

Saddlepoint approximations for the computation of survival and hazard functions are introduced in the context of parametric survival analysis. Although these approximations are computationally fast, accurate, and relatively straightforward to implement, their use in survival analysis has been lacking. We approximate survival functions using the Lugannani and Rice saddlepoint approximation to the distribution function or by numerically integrating the saddlepoint density approximation. The hazard function is approximated using the saddlepoint density and distribution functions. The approximations are especially useful for consideration of survival and hazard functions for waiting times in complicated models. Examples include total or partial waiting times for a disease that progresses through various stages (convolutions of distributions).

Aparna V. Huzurbazar

Papers

Bayesian networks for multilevel system reliability

Flowgraph Models for Multistate Time-to-Event Data

Stochastic Network Models for Survival Analysis

Accelerated Test Methods for Reliability Prediction

Survival and hazard functions for progressive diseases using saddlepoint approximations.