Name: Applied Probability and Statistical Inference in Reliability Engineering (en Inglés)
SKU: 69025676
Price: 289.36 PEN
Availability: InStock
Author: Ioannis S Triantafyllou;Alex Karagrigoriou
ISBN: 9783725883059

portada Applied Probability and Statistical Inference in Reliability Engineering (en Inglés)

Applied Probability and Statistical Inference in Reliability Engineering (en Inglés)

Formato

Libro Físico

Editorial

Mdpi AG

Autor

Ioannis S Triantafyllou;Alex Karagrigoriou

Idioma

Inglés

N° páginas

248

Encuadernación

Tapa Dura

ISBN13

9783725883059

Applied Probability and Statistical Inference in Reliability Engineering (en Inglés)

Ioannis S Triantafyllou;Alex Karagrigoriou (Autor) · Mdpi AG · Tapa Dura

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Reseña del libro "Applied Probability and Statistical Inference in Reliability Engineering (en Inglés)"

The present reprint contains the 11 articles accepted and published in the Special Issue "Applied Probability and Statistical Inference in Reliability Engineering, 2025" of the MDPI Mathematics journal, which cover a wide range of topics connected to recent methodological developments and emerging applications at the intersection of probabilistic modeling and reliability analysis. These topics include, among others, advanced probabilistic and distributional modeling for nonstandard data (including censoring, skewness, dependence, and imprecision), as well as statistical estimation and inference methods in reliability analysis. It further addresses system reliability, stochastic processes (including renewal and aging phenomena), stochastic ordering, data-driven approaches, and the integration of reliability modeling with maintenance optimization and decision-making.It is hoped that this reprint will be of interest and value to researchers working in Applied Probability and Statistical Inference in Reliability Engineering, as well as to readers with a solid mathematical background who wish to become acquainted with recent advances in probabilistic modeling, statistical methodology and their applications in reliability analysis across diverse domains.