基于马尔可夫-蒙特卡洛采样的电源网络分析

电源网络分析是深亚微米集成电路设计的关键因素之一.针对高性能芯片采用IBM C4封装和网状结构片上网络的特点,首先通过随机游走模型推导出电源网络的三个性质,接着基于这些性质给出了深亚微米电源网络的分析框架,最后提出了基于马尔可夫-蒙特卡洛采样的电源网络求解算法.仿真实验表明,与随机采样求解电源网络方程相比,马尔可夫-蒙特卡洛采样在不降低计算精度的前提下,运算速度提高了近两个数量级.     

高档数控装备多层贝叶斯可靠性评估模型研究

为实现少量故障数据样本下五轴联动数控机床精确的可靠性评估,通过对贝叶斯估计方法进行研究,设计实现数控机床可靠性建模。该方法首先设定威布尔模型参数服从伽玛分布,为解决无先验信息问题,引入两层贝叶斯方法。为解决积分求解后验分布计算困难问题,使用MCMC方法计算模型参数估计值,最终建立起威布尔模型。引用某机床一年的历史故障数据,使用所设计方法与最小二乘法实现建模,用拟合优度检验将两个方法结果进行比较判断出所设计方法具有可行性。最后依据建立的可靠性模型对机床进行可靠性评估。     

An improved Markov Chain Monte Carlo method for MIMO iterative detection and decoding

Recently, a new soft-in soft-out detection algorithm based on the Markov Chain Monte Carlo （MCMC） simulation technique for Multiple-Input Multiple-Output （MIMO） systems is proposed, which is shown to perform significantly better than their sphere decoding counterparts with relatively low complexity. However, the MCMC simulator is likely to get trapped in a fixed state when the channel SNR is high, thus lots of repetitive samples are observed and the accuracy of A Posteriori Probability （APP） estimation deteriorates. To solve this problem, an improved version of MCMC simulator, named forced-dispersed MCMC algorithm is proposed. Based on the a posteriori variance of each bit, the Gibbs sampler is monitored. Once the trapped state is detected, the sample is dispersed intentionally according to the a posteriori variance. Extensive simulation shows that, compared with the existing solution, the proposed algorithm enables the markov chain to travel more states, which ensures a near-optimal performance.     

基于AM-MCMC算法的贝叶斯概率洪水预报模型

本文在贝叶斯预报系统的框架下,利用BP网络能描述非线性映射的特性建立了基于BP网络的先验密度和似然函数的模型,并采用基于自适应采样算法(Adaptive Metropolis algorithm,简称AM)的马尔可夫链蒙特卡罗模拟方法(Markov Chain Monte Carlo,简称MCMC)求解流量的后验密度,最后给出流量的概率预报。实例表明,基于AM-MCMC的BP贝叶斯概率水文预报的精度高,且能给出预报的方差,使得防洪决策可以考虑预报的不确定性。     

水闸闸室底板地基反力的影响因素研究

在水利工程中,水闸结构是一种应用广泛的挡水和过水建筑物.尤其是对于修建于土质地基上的水闸工程,闸室结构与地基的相互作用复杂且强烈,闸室底板作为闸室与地基的衔接构件,其工作性态与水闸工程的功能发挥和运行安全密切相关.将闸室底板所受到的地基反力作为闸室结构与地基相互作用的表征指标,依据现行水闸设计规范,融合马尔可夫链蒙特卡...     

基于最优采样函数的粒子滤波算法与贝叶斯估计

传统粒子滤波器(PF)直接根据状态演化方程产生新的粒子,由于没有考虑新近观测对状态估计的影响,这种滤波器性能较差,即便在粒子数目很大的情况也是如此。为此,本文提出一种基于序贯重要采样(SIS)的改进粒子滤波算法,该算法采用集成了新近观测量的最优采样(或重要密度)函数指导粒子的生成,使粒子权值的方差最小化,能有效减轻粒子退化问题;同时。在粒子重采样之后增加了马尔科夫链蒙特卡洛(MCMC)过程,消除了重采样引起的粒子贫化的负面影响,从而使粒子的多样性得以保持。对非线性系统的状态估计和只测角跟踪的仿真实例均表明,本文所提出的算法比传统估计算法如EKF,UKF具有更高的精度和更强的鲁棒性;与标准PF相比,其性能也有较大的提高,并可以在相同的估计精度下大大减少所需的粒子数目,是一种有效的非线性滤波算法。     

On generalised asymmetric stochastic volatility models

Stochastic volatility (SV) models have been considered as a real alternative to time-varying volatility of the ARCH family. Existing asymmetric SV (ASV) models treat volatility asymmetry via the leverage effect hypothesis. Generalised ASV models that take account of both volatility asymmetry and normality violation expressed simultaneously by skewness and excess kurtosis are introduced. The new generalised ASV models are estimated using the Bayesian Markov Chain Monte Carlo approach for parametric and log-volatility estimation. By using simulated and real financial data series, the new models are compared to existing SV models for their statistical properties, and for their estimation performance in within and out-of-sample periods. Results show that there is much to gain from the introduction of the generalised ASV models.     

Bayesian skew selection for multivariate models

We develop a Bayesian approach for the selection of skew in multivariate skew t distributions constructed through hidden conditioning in the manners suggested by either Azzalini and Capitanio (2003) or Sahu et al. (2003). We show that the skew coefficients for each margin are the same for the standardized versions of both distributions. We introduce binary indicators to denote whether there is symmetry, or skew, in each dimension. We adopt a proper beta prior on each non-zero skew coefficient, and derive the corresponding prior on the skew parameters. In both distributions we show that as the degrees of freedom increases, the prior smoothly bounds the non-zero skew parameters away from zero and identifies the posterior. We estimate the model using Markov chain Monte Carlo (MCMC) methods by exploiting the conditionally Gaussian representation of the skew t distributions. This allows for the search through the posterior space of all possible combinations of skew and symmetry in each dimension. We show that the proposed method works well in a simulation setting, and employ it in two multivariate econometric examples. The first involves the modeling of foreign exchange rates and the second is a vector autoregression for intra-day electricity spot prices. The approach selects skew along the original coordinates of the data, which proves insightful in both examples.     

Bayesian variable selection for Poisson regression with underreported responses

Variable selection for Poisson regression when the response variable is potentially underreported is considered. A logistic regression model is used to model the latent underreporting probabilities. An efficient MCMC sampling scheme is designed, incorporating uncertainty about which explanatory variables affect the dependent variable and which affect the underreporting probabilities. Validation data is required in order to identify and estimate all parameters. A simulation study illustrates favorable results both in terms of variable selection and parameter estimation. Finally, the procedure is applied to a real data example concerning deaths from cervical cancer.     

A space-time filter for panel data models containing random effects

A space-time filter structure is introduced that can be used to accommodate dependence across space and time in the error components of panel data models that contain random effects. This specification provides insights regarding several space-time structures that have been used recently in the panel data literature. Markov Chain Monte Carlo methods are set forth for estimating the model which allow simple treatment of initial period observations as endogenous or exogenous. The performance of the approach is demonstrated using both Monte Carlo experiments and an applied illustration.