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About 6,008 papers for “Bayesian reliability analysis”

Bayesian Reliability Analysis

Marvin Rausand, Arnljot Høyland · Wiley series in probability and statistics · 1994

The prelims comprise: Introduction Basic Concepts Bayesian Point Estimation Credibility Interva Choice of Prior Distribution Bayesian Life Test Sampling Plans Interpretation of the Prior Distribution The Predictive Density Problems

Cited by 40

Reliability analysis using Bayesian networks

Ayeley Tchangani · Open Archive Toulouse Archive Ouverte (University of Toulouse) · 2001

This paper considers the problem of reliability analysis of that consists groups of components organized in parallel and/or in serial given the reliability of the basic component. The mathematical tool used to tackle this problem is that of Bayesian networks (BN) that derive from convergence of statistics and Artificial Intelligence (AI). It consists of the representation of probabilistic causal relation between variables of a system.

Cited by 13Open access

Modeling Uncertain Bayesian System Reliability Analysis

Mahsa Mirzayi, Reza Zarei, Gholamhossein Yari et al. · IEEE Access · 2024

Classical system reliability analysis is based largely on crisp (also called “precise”) lifetime data. However, in practical applications, due to the lack, inaccuracy, and fluctuation of collected data, such information are often imprecise and expressed in the form of fuzzy quantities. Therefore, it is necessary to reformulate the conventional methods to imprecise environments for studying and analyzing the systems of interests. On the other hand, Bayesian approaches have shown to be useful for small data samples, especially when there is some prior information about the underlying model. Most reported studies in this area deal with obtaining the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\alpha $ </tex-math></inline-formula>-cuts of system reliability estimator which given a lower and upper bound for system reliability. This article, however, proposes a new method for Bayesian estimation of system reliability based on <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\alpha $ </tex-math></inline-formula>-pessimistic approach. To do this, we use the definition of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\alpha $ </tex-math></inline-formula>-pessimistic and existing prior information about the unknown parameter under investigation. Moreover, to employ the Bayesian approach, model parameters are assumed to be fuzzy random variables with fuzzy prior distributions. Two practical examples are provided to clarify the proposed method.

Cited by 2Open access

On Bayesian power analysis in reliability

Fulvio De Santis, Stefania Gubbiotti, Francesco Mariani · Archivio istituzionale della ricerca (Alma Mater Studiorum Università di Bologna) · 2023

Evaluation of reliability of a production process is a crucial step in sustainability assessment. In this article we consider the sample size determination problem when time-to-failure is modeled by a Rayleigh distribution. Following a hybrid Bayesian-frequentist approach, the selection of the number of units is based on the so-called probability of success (PoS) of the experiment, that is the expected value of the power function with respect to a design prior distribution for the mean failure time. This method works properly only if PoS is a representative summary of the distribution of the power function induced by the design prior. Therefore we derive and analyze the density of the power function for one-sided tests on the Rayleigh parameter, using conjugate design priors. Numerical examples are discussed.

Cited by 0

Bayesian analysis of the reliability of peritoneal lavage.

Vic Velanovich · PubMed · 1990

The popular methods of reporting the reliability of a test--sensitivity and specificity--do not tell the practicing physician what the chance is that a patient has the disease if the results from the particular test for that disease are positive or negative. Bayesian analysis, however, does answer this question. In this study, the data from 29 studies of peritoneal lavage were analyzed using the Bayesian method. The probability of an intra-abdominal injury being present if findings from lavage were positive ranged from 0.448 to 1.0. The probability of an intra-abdominal injury being present if findings from lavage were negative ranged from zero to 0.286. These probabilities varied with the criteria for positivity, technique used and type of trauma. In general, results of Bayesian analysis showed that findings from peritoneal lavage were most reliable in diagnosing the patient with blunt trauma when the standard positive criteria and open technique were used.

Cited by 7

Reliability analysis of embankment dams using Bayesian network

Haicheng Li · 2009

It is increasing recognized by decision makers that efficient management and consistent quantification of risks associated with large dams is an issue of societal concern. The book entitled Risk and Uncertainty in Dam Safety provides details of the analysis methods available to characterize and quantify the risks and uncertainties in dam safety (Hartford and Baecher 2004). The reliability and risk of dams has been extensively studied in the literature. For example, Benjamin (1983) explored the application of statistical decision theory to the dams and levees. Lave and Balvanyos (1998) studied the risk analysis and risk management using benefit-cost analysis. In recent years, Bayesian network (BN) (Jensen 1996; Jensen 2000) has received considerable interest for risk assessments and as a decision support tool for technical systems. For instance, Friis-Hansen (2001) explored the application of BN on marine structures. Faber et al. (2002) studied the risk assessment of decommissioning options using BN. Bayraktarli et al. (2005) proposed a framework for the assessment and management of earthquake risks based on BN, where optimal risk mitigation actions are identified based on indicators. Holichy (2005) developed a BN for fire safety design situations. Straub (2005) explored the application of BN for natural hazards risk assessment.

Cited by 1