Titlebook: Bayesian Inference and Computation in Reliability and Survival Analysis; Yuhlong Lio,Ding-Geng Chen,Tzong-Ru Tsai Book 2022 The Editor(s)

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Book 2022issues, with emphasis on applications to reliability and survival analysis. Topics covered are timely and have the potential to influence the interacting worlds of biostatistics, engineering, medical sciences, statistics, and more.. The included chapters present current methods, theories, and applic

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Human Development in Bihar, India priors are carried out to evaluate the performance of the Bayesian estimation in terms of bias and root mean square error. A real data on samples of grease-based magnetorheological fluids is analyzed for illustration of the Bayesian estimation.

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https://doi.org/10.1007/978-3-658-26798-8modeled nonparametrically by assigning a Gamma process prior. Efficient Gibbs samplers are developed for the posterior computation under these three models for the two types of data. The proposed methods are evaluated in a simulation study and illustrated by three real-life data applications.

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Bayesian Analysis of Stochastic Processes in Reliabilityas the missing data or uncertain data problem. The Bayesian approach relying on prior belief or expertise appears to be a natural tool in such situations. Thus the Bayesian approach provides efficient methods for reliability analysis with stochastic processes. The objective of this chapter is to des