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Stochastic Modeling and Statistical Methods

Advances and Applications

Specificaties
Paperback, blz. | Engels
Elsevier Science | e druk, 2025
ISBN13: 9780443316944
Rubricering
Elsevier Science e druk, 2025 9780443316944
Onderdeel van serie Advances in Reliability Science
Verwachte levertijd ongeveer 9 werkdagen

Samenvatting

Stochastic Modeling and Statistical Methods: Advances and Applications is the practical guide to the latest developments in data analysis and research methods. The book explores the significant research progress that has been seen in recent decades, offering vital tools for analyzing modern applications and real data. Topics covered include Dynamic Reliability, Stochastic Modeling, System Maintainability, and Parametric, Semi-Parametric, and Nonparametric Statistical Inference. Readers will find the latest advancements in these areas, making it an essential resource for researchers and practitioners who want to explore these evolving fields and stay updated on cutting-edge research.

Specificaties

ISBN13:9780443316944
Taal:Engels
Bindwijze:Paperback

Inhoudsopgave

1. Inference of one-shot devices with Weibull component lifetimes under Gamma frailty model<br>2. Signature Reliability Detection and Performance Optimization of Low Cost Autonomous Robot Vacuum Cleaner Via-UGFT<br>3. The &radic;n-consistency of the empirical estimator of stationary probability of semi-Markov chains<br>4. Alternative transient solutions for semi-Markov redundant systems in Reliability Engineering<br>5. Parametric estimation of censored semi-Markov chains<br>6. Understanding a system&rsquo;s performance in the presence of k-out-of-n: F and standby redundancy A reliability approach through Markov process<br>7. On some occurrence rates for Markov processes with application<br>8. Interval-censored reliability tests under lognormal lifetimes<br>9. Selective Review of Penalized Learning Methods for Event Processes<br>10. Hidden Markov Models for Aviation Prognostics<br>11. Stochastic modeling of the elastic properties of carbon-fiber-reinforced 3D printed filaments using polynomial chaos expansion<br>12. Stochastic Functionally Pooled Models for Diagnostics and Prognostics in Engineering<br>13. The application of Drifting Markov Modelling to Dynamics Skill Acquisition<br>14. A multi-granularity smart rejuvenation framework for a two-unit series system<br>15. Fitting a managed population model using ABC

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        Stochastic Modeling and Statistical Methods