Bayesian Inference and Computation in Reliability and Survival Analysis
Bayesian analysis is one of the important tools for statistical modelling and inference. Bayesian frameworks and methods have been successfully applied to solve practical problems in reliability and survival analysis, which have a wide range of real world applications in medical and biological sciences, social and economic sciences, and engineering. In the past few decades, significant developments of Bayesian inference have been made by many researchers, and advancements in computational technology and computer performance has laid the groundwork for new opportunities in Bayesian computation for practitioners.
Because these theoretical and technological developments introduce new questions and challenges, and increase the complexity of the Bayesian framework, this book brings together experts engaged in groundbreaking research on Bayesian inference and computation to discuss important issues, 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 applications in the diverse area of biostatistical analysis. The volume as a whole serves as reference in driving quality global health research.
|Reihe||Emerging Topics in Statistics and Biostatistics|
|Ausgabe||1st ed. 2022|
|Genre||Mathematik/Wahrscheinlichkeitstheorie, Stochastik, Mathematische Statistik|
|Verlag||Springer International Publishing|
|Herausgegeben von||Yuhlong Lio, Ding-Geng Chen, Hon Keung Tony Ng, Tzong-Ru Tsai|