Application of a generalized lognormal distribution to engineering data fitting
نویسنده
چکیده
The lognormal distribution is commonly used to model certain types of data that arise in several fields of engineering as, for example, different types of lifetime data or coefficients of wear and friction. However, a generalized form of the lognormal distribution can be used to provide better fits for many types of experimental or observational data. In this paper, a Bayesian analysis of a generalized form of the lognormal distribution is developed. Bayesian inference offers the possibility of taking expert opinions into account. This makes this approach appealing in practical problems concerning many fields of knowledge, including reliability of technical systems. The full Bayesian analysis includes a Gibbs sampling algorithm to obtain the samples from the posterior distribution of the parameters of interest. Empirical proofs over a wide range of engineering data sets have shown that the generalized lognormal distribution can outperform the lognormal one in this Bayesian context.
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Bayesian analysis of a generalized lognormal distribution
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