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Home  >  Volume 27 (July 2014)

29. Applying Bayesian Model Averaging in Reliability Concept by Iyiegbu T.I, Asiribo O.E and Dikko H.G. Volume27, (July, 2014), pp 235 – 240
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Abstract

Reliability is the probability that a system will perform its intended function for a specified period of time under a given set of conditions. Bayesian techniques have always proved to be a good method in analyzing data from experimental design. This paper treats reliability as a single fixed number whose unknown value is to be estimated. Reliability has uncertainty associated with it, this thus require us to treat it as a random variable and therefore discuss it using probability distribution, f(R). Also in this paper, Bayesian model selection in R package is used to average all the 2k possible models and thereby identify the correct model that best define the reliability improvement. The superior analytical power of this approach compared to other methods like OLS was shown.

Keywords: Fuzzy time series, Autoregressive integrated moving average, Theil’s regression, Mean squared forecast     error, Root mean square forecast error and Coefficient of determination


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