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基于后验预测分布的机器人焊接质量监控研究 被引量:4
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作者 吴姝 何雨飞 +1 位作者 屈挺 胡楷雄 《机床与液压》 北大核心 2022年第9期7-12,共6页
针对KUKA机器人焊接质量监测工作量大、抽样样本小的特点,提出一种基于后验预测分布的贝叶斯动态监控方法。从历史数据中选择合适的数据,计算先验分布的超参数;再结合当前样本构建服从负二项分布的后验预测分布,实时计算控制限,实现对... 针对KUKA机器人焊接质量监测工作量大、抽样样本小的特点,提出一种基于后验预测分布的贝叶斯动态监控方法。从历史数据中选择合适的数据,计算先验分布的超参数;再结合当前样本构建服从负二项分布的后验预测分布,实时计算控制限,实现对焊接质量的动态监测。结果表明:该方法优于传统似然估计法,有更强的异常检出力和稳健性。 展开更多
关键词 焊接机器人 质量控制 贝叶斯理论 后验预测分布
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Bayesian Posterior Predictive Probability Happiness
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作者 Gabriela Rodríguez-Hernández Galileo Domínguez-Zacarías Carlos Juárez Lugo 《Applied Mathematics》 2016年第8期753-764,共12页
We propose to determine the underlying causal structure of the elements of happiness from a set of empirically obtained data based on Bayesian. We consider the proposal to study happiness as a multidimensional constru... We propose to determine the underlying causal structure of the elements of happiness from a set of empirically obtained data based on Bayesian. We consider the proposal to study happiness as a multidimensional construct which converges four dimensions with two different Bayesian techniques, in the first we use the Bonferroni correction to estimate the mean multiple comparisons, on this basis it is that we use the function t and a z-test, in both cases the results do not vary, so it is decided to present only those shown by the t test. In the Bayesian Multiple Linear Regression, we prove that happiness can be explained through three dimensions. The technical numerical used is MCMC, of four samples. The results show that the sample has not atypical behavior too and that suitable modifications can be described through a test. Another interesting result obtained is that the predictive probability for the case of sense positive of life and personal fulfillment dimensions exhibit a non-uniform variation. 展开更多
关键词 Bayesian Inference posterior predictive distribution MCMC HAPPINESS
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基于后验预测分布的贝叶斯模型评价及其在霍乱传染数据中的应用 被引量:1
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作者 徐继承 王婷 +4 位作者 黄水平 赵华硕 金英良 王可 曾平 《郑州大学学报(医学版)》 CAS 北大核心 2015年第2期167-171,共5页
目的:探讨基于后验预测分布的贝叶斯模型评价方法。方法:采用贝叶斯ZIP模型和Possion模型分析霍乱传染数据,通过后验预测分布评价2个模型的拟合优度。结果:如果以数据中0的家庭数为差别检验统计量,则Poisson模型和ZIP模型的后验预测P值... 目的:探讨基于后验预测分布的贝叶斯模型评价方法。方法:采用贝叶斯ZIP模型和Possion模型分析霍乱传染数据,通过后验预测分布评价2个模型的拟合优度。结果:如果以数据中0的家庭数为差别检验统计量,则Poisson模型和ZIP模型的后验预测P值分别为0.038和0.503。如果以χ2为差别检验统计量,则Poisson模型和ZIP模型的后验预测P值分为0.005和0.476。结论:ZIP模型对霍乱传染数据拟合良好,而Possion模型拟合不足。 展开更多
关键词 后验预测分布 模型评价 贝叶斯ZIP模型
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