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Dang, C.; Valdebenito, M.A.; Faes, M.G.R.; Wei, P.F.; Beer, M. |
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Title |
Structural reliability analysis: A Bayesian perspective |
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Year |
2022 |
Publication |
Structural Safety |
Abbreviated Journal |
Struct. Saf. |
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Volume |
99 |
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102259 |
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Keywords |
Failure probability; Bayesian inference; Gaussian process; Numerical uncertainty; Parallel computing |
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Abstract |
Numerical methods play a dominant role in structural reliability analysis, and the goal has long been to produce a failure probability estimate with a desired level of accuracy using a minimum number of performance function evaluations. In the present study, we attempt to offer a Bayesian perspective on the failure probability integral estimation, as opposed to the classical frequentist perspective. For this purpose, a principled Bayesian Failure Probability Inference (BFPI) framework is first developed, which allows to quantify, propagate and reduce numerical uncertainty behind the failure probability due to discretization error. Especially, the posterior variance of the failure probability is derived in a semi-analytical form, and the Gaussianity of the posterior failure probability distribution is investigated numerically. Then, a Parallel Adaptive-Bayesian Failure Probability Learning (PA-BFPL) method is proposed within the Bayesian framework. In the PA-BFPL method, a variance-amplified importance sampling technique is presented to evaluate the posterior mean and variance of the failure probability, and an adaptive parallel active learning strategy is proposed to identify multiple updating points at each iteration. Thus, a novel advantage of PA-BFPL is that both prior knowledge and parallel computing can be used to make inference about the failure probability. Four numerical examples are investigated, indicating the potential benefits by advocating a Bayesian approach to failure probability estimation. |
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0167-4730 |
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WOS:000837863500001 |
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UAI @ alexi.delcanto @ |
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1637 |
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Author |
de la Cruz, R.; Fuentes, C.; Padilla, O. |
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Title |
A Bayesian Mixture Cure Rate Model for Estimating Short-Term and Long-Term Recidivism |
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Year |
2023 |
Publication |
Entropy |
Abbreviated Journal |
Entropy |
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25 |
Issue |
1 |
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56 |
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Keywords |
Bayesian inference; MCMC methods; mixture cure rate models; recidivism; Weibull distribution |
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Abstract |
Mixture cure rate models have been developed to analyze failure time data where a proportion never fails. For such data, standard survival models are usually not appropriate because they do not account for the possibility of non-failure. In this context, mixture cure rate models assume that the studied population is a mixture of susceptible subjects who may experience the event of interest and non-susceptible subjects that will never experience it. More specifically, mixture cure rate models are a class of survival time models in which the probability of an eventual failure is less than one and both the probability of eventual failure and the timing of failure depend (separately) on certain individual characteristics. In this paper, we propose a Bayesian approach to estimate parametric mixture cure rate models with covariates. The probability of eventual failure is estimated using a binary regression model, and the timing of failure is determined using a Weibull distribution. Inference for these models is attained using Markov Chain Monte Carlo methods under the proposed Bayesian framework. Finally, we illustrate the method using data on the return-to-prison time for a sample of prison releases of men convicted of sexual crimes against women in England and Wales and we use mixture cure rate models to investigate the risk factors for long-term and short-term survival of recidivism. |
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1099-4300 |
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WOS:000914983600001 |
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UAI @ alexi.delcanto @ |
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1720 |
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