针对传统假设中个体寿命独立同分布的不足,构建了贝叶斯Weibull共享异质性模型,提出了对寿命服从Weibull分布的产品,运用基于Gibbs抽样的马尔可夫链蒙特卡罗(Markov chain Monte Carlo,MCMC)方法动态模拟出参数后验分布的马尔可夫链,在...针对传统假设中个体寿命独立同分布的不足,构建了贝叶斯Weibull共享异质性模型,提出了对寿命服从Weibull分布的产品,运用基于Gibbs抽样的马尔可夫链蒙特卡罗(Markov chain Monte Carlo,MCMC)方法动态模拟出参数后验分布的马尔可夫链,在异质性因子的先验分布为Gamma分布时,给出随机截尾条件下,参数在Weibull共享异质性模型中的贝叶斯估计,提高了计算的精度。借助数据仿真说明了利用WinBUGS(Bayesianinference using Gibbs sampling)软件包进行建模分析的过程,证明了该模型在可靠性应用中的直观性与有效性。展开更多
在相关系数平稳过程的基础上,提出了基于MCMC(Markov Chain Monte Carlo)方法来估计多维相关系数平稳序列模型的参数;给出基于贝叶斯估计的多维相关系数平稳序列模型参数的算法;在无先验信息条件下,模拟验证了用此方法估计二维相关系数...在相关系数平稳过程的基础上,提出了基于MCMC(Markov Chain Monte Carlo)方法来估计多维相关系数平稳序列模型的参数;给出基于贝叶斯估计的多维相关系数平稳序列模型参数的算法;在无先验信息条件下,模拟验证了用此方法估计二维相关系数平稳序列模型参数的有效性。展开更多
In this paper, we introduce a novel method for facial landmark detection. We localize facial landmarks according to the MAP crite rion. Conventional gradient ascent algorithms get stuck at the local optimal solution. ...In this paper, we introduce a novel method for facial landmark detection. We localize facial landmarks according to the MAP crite rion. Conventional gradient ascent algorithms get stuck at the local optimal solution. Gibbs sampling is a kind of Markov Chain Monte Carlo （MCMC） algorithm. We choose it for optimization because it is easy to implement and it guarantees global conver gence. The posterior distribution is obtained by learning prior distribution and likelihood function. Prior distribution is assumed Gaussian. We use Principle Component Analysis （PCA） to reduce the dimensionality and learn the prior distribution. Local Linear Support Vector Machine （LLSVM） is used to get the likelihood function of every key point. In our experiment, we compare our de tector with some other wellknown methods. The results show that the proposed method is very simple and efficient. It can avoid trapping in local optimal solution.展开更多
A new method for structural physical parameter identification is proposed for linear structure.Firstly,a linear structural identification model was obtained based on a series of transformation of the dynamic character...A new method for structural physical parameter identification is proposed for linear structure.Firstly,a linear structural identification model was obtained based on a series of transformation of the dynamic characteristic equation.Then the posterior distribution of the model is obtained by the Bayesian updating theory.Using the structural modal parameters and considering their randomness,the structural stiffness parameter is obtained from the conditional posterior distribution of the linear structural identification model.The Gibbs sampling based on the Markov Chain Monte Carlo(MCMC)method is employed during the process.In order to illustrate the proposed method,a 3-DOF linear shear building is used as an example to detect and quantify its damage based on model data measured before and after a severe loading event.The research shows that damage level and locations can be identified with little error by using proposed method.展开更多
针对采用Rao-Blackwellized粒子滤波器的移动机器人同步定位与地图构建算法(RBPF-SLAM)所面临的粒子退化问题,提出了一种改进的采样方法。该方法在原有采样方法的基础上,加入一个用Gibbs采样实现的向后MCMC(Markov chain Monte Carlo)...针对采用Rao-Blackwellized粒子滤波器的移动机器人同步定位与地图构建算法(RBPF-SLAM)所面临的粒子退化问题,提出了一种改进的采样方法。该方法在原有采样方法的基础上,加入一个用Gibbs采样实现的向后MCMC(Markov chain Monte Carlo)移动步骤,利用当前新获取的信息对机器人路径样本的最后一段进行调整,从而降低了样本退化的可能性。对比仿真实验验证了该方法的有效性。展开更多
文摘针对传统假设中个体寿命独立同分布的不足,构建了贝叶斯Weibull共享异质性模型,提出了对寿命服从Weibull分布的产品,运用基于Gibbs抽样的马尔可夫链蒙特卡罗(Markov chain Monte Carlo,MCMC)方法动态模拟出参数后验分布的马尔可夫链,在异质性因子的先验分布为Gamma分布时,给出随机截尾条件下,参数在Weibull共享异质性模型中的贝叶斯估计,提高了计算的精度。借助数据仿真说明了利用WinBUGS(Bayesianinference using Gibbs sampling)软件包进行建模分析的过程,证明了该模型在可靠性应用中的直观性与有效性。
基金supported by ZTE Industry-Academia-Research Cooperation Funds
文摘In this paper, we introduce a novel method for facial landmark detection. We localize facial landmarks according to the MAP crite rion. Conventional gradient ascent algorithms get stuck at the local optimal solution. Gibbs sampling is a kind of Markov Chain Monte Carlo （MCMC） algorithm. We choose it for optimization because it is easy to implement and it guarantees global conver gence. The posterior distribution is obtained by learning prior distribution and likelihood function. Prior distribution is assumed Gaussian. We use Principle Component Analysis （PCA） to reduce the dimensionality and learn the prior distribution. Local Linear Support Vector Machine （LLSVM） is used to get the likelihood function of every key point. In our experiment, we compare our de tector with some other wellknown methods. The results show that the proposed method is very simple and efficient. It can avoid trapping in local optimal solution.
文摘A new method for structural physical parameter identification is proposed for linear structure.Firstly,a linear structural identification model was obtained based on a series of transformation of the dynamic characteristic equation.Then the posterior distribution of the model is obtained by the Bayesian updating theory.Using the structural modal parameters and considering their randomness,the structural stiffness parameter is obtained from the conditional posterior distribution of the linear structural identification model.The Gibbs sampling based on the Markov Chain Monte Carlo(MCMC)method is employed during the process.In order to illustrate the proposed method,a 3-DOF linear shear building is used as an example to detect and quantify its damage based on model data measured before and after a severe loading event.The research shows that damage level and locations can be identified with little error by using proposed method.
文摘针对采用Rao-Blackwellized粒子滤波器的移动机器人同步定位与地图构建算法(RBPF-SLAM)所面临的粒子退化问题,提出了一种改进的采样方法。该方法在原有采样方法的基础上,加入一个用Gibbs采样实现的向后MCMC(Markov chain Monte Carlo)移动步骤,利用当前新获取的信息对机器人路径样本的最后一段进行调整,从而降低了样本退化的可能性。对比仿真实验验证了该方法的有效性。
