Towards few-shot deepfake detection with an enhanced CLIP model

Yang, Yumin

doi:10.1016/j.neunet.2026.109027

Statistical inference on stress-strength reliability of cold standby systems using discrete phase type distribution

2026-03-01

0

PRE

AI

Y

Yuan Xu

H

Hu, Linmin *

M

Mingjia Li

DOI：10.1080/00949655.2026.2630233

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In this paper, an innovative reliability model of the system level-cold standby system is developed. This model integrates discrete phase type (DPH) distributions into the estimation of stress-strength reliability (SSR). The SSR expression for the system is derived by utilizing probability analysis and calculation. The estimators for the SSR are theoretically deduced by the maximum likelihood estimation (MLE) and Bayesian inference. The maximum likelihood estimates are obtained using the Expectation-Maximization (EM) algorithm, while the Gibbs sampling (GS) method is employed for the Bayesian inference. The deviations between these estimates and the actual values are visually illustrated in numerical experiments.

Keywords:

Reliability

cold standby

discrete phase type distribution

Bayesian inference

EM algorithm

Statistical inference on stress-strength reliability of cold standby systems using discrete phase type distribution

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