SEMI-BLIND CHANNEL ESTIMATION OF MULTIPLE-INPUT/MULTIPLE-OUTPUT SYSTEMS BASED ON MARKOV CHAIN MONTE CARLO METHODS

This paper addresses the issues of channel estimation in a Multiple-Input/Multiple-Output (MIMO) system. Markov Chain Monte Carlo (MCMC) method is employed to jointly estimate the Channel State Information (CSI) and the transmitted signals. The deduced algorithms can work well under circumstances of low Signal-to-Noise Ratio (SNR). Simulation results are presented to demonstrate their effectiveness.

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.

Markov Chain Monte Carlo Solution of Laplace’s Equation in Axisymmetric Homogeneous Domain

With increasing complexity of today’s electromagnetic problems, the need and opportunity to reduce domain sizes, memory requirement, computational time and possibility of errors abound for symmetric domains. With several competing computational methods in recent times, methods with little or no iterations are generally preferred as they tend to consume less computer memory resources and time. This paper presents the application of simple and efficient Markov Chain Monte Carlo (MCMC) method to the Laplace’s equation in axisymmetric homogeneous domains. Two cases of axisymmetric homogeneous problems are considered. Simulation results for analytical, finite difference and MCMC solutions are reported. The results obtained from the MCMC method agree with analytical and finite difference solutions. However, the MCMC method has the advantage that its implementation is simple and fast.

Arriving at estimates of a rate and state fault friction model parameter using Bayesian inference and Markov chain Monte Carlo

The critical slip distance in rate and state model for fault friction in the study of potential earthquakes can vary wildly from micrometers to few me-ters depending on the length scale of the critically stressed fault.This makes it incredibly important to construct an inversion framework that provides good estimates of the critical slip distance purely based on the observed ac-celeration at the seismogram.To eventually construct a framework that takes noisy seismogram acceleration data as input and spits out robust estimates of critical slip distance as the output,we first present the performance of the framework for synthetic data.The framework is based on Bayesian inference and Markov chain Monte Carlo methods.The synthetic data is generated by adding noise to the acceleration output of spring-slider-damper idealization of the rate and state model as the forward model.

结合贝叶斯-MCMC更新的RC梁抗剪承载力概率模型的可靠度分析

为分析钢筋混凝土(RC)梁抗剪承载力,首先,基于国内外100组RC梁抗剪承载力试验数据作为先验信息,结合我国GB 50010-2010规范的抗剪承载力,综合考虑混凝土与箍筋的材料强度、截面尺寸、剪跨比、配箍率等因素的影响,通过贝叶斯-马尔科夫链蒙特卡洛(MCMC)方法对我国规范模型进行更新与修正;随后,结合Monte Carlo模拟,对所建立的RC梁概率抗剪承载力模型进行可靠度分析,验证了该模型具有较好的计算精度与可靠性。结果表明:概率模型均值与试验值比值K的均值与标准差分别为1.013与0.171,试验值落在概率模型的95%置信区间范围内,且可靠指标分布在4.0左右,说明建立的概率模型具有较好的预测效果与可靠性。

随机利率影响下信用风险的Monte-Carlo模拟

在前人利用马尔科夫链表示公司信用等级的基础上,将信用等级和随机利率引入离散时间的信用风险模型中,从而提出随机利率影响下的新的信用风险模型。就上述模型,对不同初始信用等级、初始盈余以及不同时刻的破产概率进行Monte-Carlo模拟,并讨论了相同条件下初始盈余与破产概率、初始信用等级与破产概率以及时间长短与破产概率之间的相互关系。

基于MCMC的电网安全稳定控制系统动态可靠性评估方法

现有安全稳定控制系统(简称稳控系统)的可靠性评估方法本质上属于静态建模,由于未能体现系统内各装置老化和检修等动态过程,在一定程度上影响了评估结果的准确性。为此,文中提出一种基于马尔可夫链蒙特卡洛(Markov chain Monte Carlo,MCMC)的稳控系统动态可靠性评估方法。首先针对失效过程,构建四状态非齐次马尔可夫模型来模拟装置老化过程,并给出各状态评判方法;其次针对修复过程,分析不同检修策略对装置状态转移的影响以体现状态检修的差异性;最后考虑稳控装置状态转移过程的时序或条件相关性,对稳控系统可靠性进行动态建模。以实际稳控系统为例,仿真对比不同检修策略下的可靠性,并对模型参数进行灵敏度分析。评估结果表明,该方法可以求解稳控系统的时变可用度,用于指导稳控装置现场合理检修。

FAST CONVERGENT MONTE CARLO RECEIVER FOR OFDM SYSTEMS

The paper investigates the problem of the design of an optimal Orthogonal Fre- quency Division Multiplexing (OFDM) receiver against unknown frequency selective fading. A fast convergent Monte Carlo receiver is proposed. In the proposed method, the Markov Chain Monte Carlo (MCMC) methods are employed for the blind Bayesian detection without channel es- timation. Meanwhile, with the exploitation of the characteristics of OFDM systems, two methods are employed to improve the convergence rate and enhance the efficiency of MCMC algorithms. One is the integration of the posterior distribution function with respect to the associated channel parameters, which is involved in the derivation of the objective distribution function; the other is the intra-symbol differential coding for the elimination of the bimodality problem resulting from the presence of unknown fading channels. Moreover, no matrix inversion is needed with the use of the orthogonality property of OFDM modulation and hence the computational load is significantly reduced. Computer simulation results show the effectiveness of the fast convergent Monte Carlo receiver.

基于MCMC填补的SSA-SVM煤与瓦斯突出预测模型

为提升煤与瓦斯突出预测准确度,减小数据缺失对煤与瓦斯突出预测的不利影响,提出1种基于链式多重填补马尔科夫链蒙特卡罗(MCMC)的麻雀搜索算法(SSA)优化支持向量机(SVM)预测模型。根据突出影响因素选取模型参数,运用MCMC对突出事故缺失值进行数据填补,采用SSA优化SVM,建立MCMC-SSA-SVM模型对填补后数据集进行预测,验证MCMC填补有效性和SSA优化性能;分别构建SVM、SSA-SVM、PSO-SVM、GAM-SVM、CMC-SVM、MCMC-PSO-SVM和MCMC-GA-SVM这7种模型进行突出预测,对比预测准确度,分析MCMC-SSA-SVM、MCMC-PSO-SVM和MCMC-GA-SVM的适应度。研究结果表明:MCMC填补后准确度均提升7.89个百分点以上,SSA的优化性能强于PSO和GA,MCMC-SSA-SVM预测准确度最高,为97.37%,泛化能力优于对比模型。研究结果可为煤与瓦斯突出预测研究提供借鉴和参考。

基于LIM-MCMC模型研究江苏近海北部海域食物网能量流动特征

食物网结构特征和能量流动的研究,对于维持海洋生态系统结构和功能的稳定具有重要意义,有助于深入理解海洋生态系统的复杂过程。本研究基于2019-2021年在江苏近海北部海域开展的季节性渔业资源底拖网调查数据,通过构建基于蒙特卡罗马尔科夫链算法的逆线性模型(Linear Inverse Models using a Monte Carlo Method Coupled with Markov Chain, LIM-MCMC),结合生态网络分析(Ecological Network Analysis,ENA)的方法,分析了该海域生态系统状态和食物网能量流动特征,旨在为江苏近海北部海域食物网营养动力学研究提供参考依据。结果表明,该海域生态系统共包含299条能量流动路径,能量流动分布整体呈典型的金字塔结构,各功能群呼吸消耗和流入有机碎屑的能量保持同步性。通过与其他海域比较发现,江苏近海北部海域生态系统的连接指数(Connectance,C)和系统杂食指数(System Omnivory Index,SOI)分别为0.40和0.22,处于较高水平,表明该生态系统不同营养级间的营养联系较为紧密,食物网结构相对复杂,能够在较大程度上抵御外界扰动。总初级生产力/总呼吸(Total Primary Production/Total Respiration,TPP/TR)和Finn’s循环指数(Finn’s Cycling Index,FCI)分别为1.05和5.76%,表明该生态系统对能量利用效率较高。此外,约束效率(Constraint Efficiency,CE)、发展程度(Extent of Development,AC)、协同效应指数(Synergism Index,b/c)和主导间接效应(Dominance Indirect Effects,i/d)也表明该生态系统具有较高的系统发展程度、再生潜力和系统发展空间。本研究将有助于为江苏近海北部海域生态系统的修复和渔业资源的可持续利用提供理论基础,为实施基于生态系统的渔业管理提供科学依据。

Bayesian-MCMC-based parameter estimation of stealth aircraft RCS models

When modeling a stealth aircraft with low RCS（Radar Cross Section）, conventional parameter estimation methods may cause a deviation from the actual distribution, owing to the fact that the characteristic parameters are estimated via directly calculating the statistics of RCS. The Bayesian–Markov Chain Monte Carlo（Bayesian-MCMC） method is introduced herein to estimate the parameters so as to improve the fitting accuracies of fluctuation models. The parameter estimations of the lognormal and the Legendre polynomial models are reformulated in the Bayesian framework. The MCMC algorithm is then adopted to calculate the parameter estimates. Numerical results show that the distribution curves obtained by the proposed method exhibit improved consistence with the actual ones, compared with those fitted by the conventional method. The fitting accuracy could be improved by no less than 25% for both fluctuation models, which implies that the Bayesian-MCMC method might be a good candidate among the optimal parameter estimation methods for stealth aircraft RCS models.

BIC与MCMC基准剂量估计模型的比较研究

目的比较BIC估计法与MCMC近似法两种后验概率法在贝叶斯基准剂量估计中的稳健性,并为山西省洪洞县儿童羟基代谢物可接受剂量的制定提供参考建议。方法首先介绍基于BIC估计法和MCMC近似法计算后验权重的原理,模拟研究选用Integrated Risk Information System数据库中不同剂量-反应数据集共30个,分析比较两种方法的优劣,并在实例研究中采用权重法进行数据整合。结果模拟研究结果显示在所研究的30个数据集中BIC估计法在BMR为0.01时有4个数据集出现BMDL预测失败的情况,在BMR为0.001时有1个数据集出现BMD预测失败的情况,以及6个数据集出现BMDL预测失败的情况。MCMC近似法计算的BMD/BMDL在每一种模型都有70%以上的数据集高于BIC估计法得到的BMD/BMDL。实例分析表明符合洪洞县儿童体内羟基代谢物剂量-反应关系的模型有linear(P=0.13,β=14.3%)、logistic(P=0.06,β=9.5%)、Weibull(P=0.14,β=10.6%)、multistage(P=0.15,β=31.1%)、Hill(P=0.21,β=34.6%)。在BMR为0.001的情况下,洪洞县儿童体内八种羟基代谢物(2-OHN、1-OHN、9-OHF、2-OHF、2-OHphe、1-OHphe、1-OHBaP、3-OHBaP)的可接受剂量(μmol/mol)依次为0.577μmol/mol、1.546μmol/mol、8.135μmol/mol、0.359μmol/mol、0.120μmol/mol、0.098μmol/mol、0.044μmol/mol、0.003μmol/mol。结论MCMC近似法在BMD估计中具有较好的稳定性和鲁棒性。

基于Markov Chain Monte Carlo模型对医院出院病人调查表数据缺失的填补与分析

目的对医院出院病人调查表普遍存在的数据缺失进行填补与分析,以保证统计调查表的质量,为医院以及上级卫生部门了解现状,进行预策和决策提供技术支持和质量保证。方法运用SAS9.1,采用多重填补方法Markov Chain Monte Carlo(MCMC)模型对缺失数据进行多次填补并综合分析。结果MCMC填补10次的结果最优。结论(Multiple Imputation)MI方法在解决医院出院病人调查表数据缺失时有优势,发挥空间较大,且填补效率较高。

基于MCMC方法的子通道程序模型参数不确定性量化分析研究

假定模型参数的不确定性服从正态分布,根据贝叶斯原理,其最可能的分布是结合先验信息和观测信息得到的最大后验概率,马尔科夫链蒙特卡罗(Markov Chain Monte Carlo,MCMC)抽样适用于此类反问题求解。鉴于随机论方法的巨大计算量,本研究利用BP(Back Propagation)神经网络及相对熵最小化来自适应加密训练数据,从而建立替代复杂正向程序的代理模型,并利用开发的不确定性分析程序对影响空泡份额的模型参数不确定性进行量化分析,选用的子通道程序为COBRA-IV。结果表明:在求得模型参数不确定性后,通过不确定性正向传递得到结果的95%置信区间对实验值的包络性较好,利用不确定性均值对模型进行标定得到的结果较基准值更接近实验值。因此,本研究建立的不确定性量化分析方法能较好适用于子通道程序的不确定性分析。

Using MCMC Probit Model to Value Coastal Beach Quality Improvement

Dichotomous choice elicitation technique of contingent valuation method is broadly used in the research fields of environmental resource and recreational activity management. The binary choice type of questions are generally analyzed by using Logit or Probit probability distribution models in which a common analysis procedure is to apply MLE for estimating variable parameters before calculating the respondents’ willingness to pay. In this paper, a MCMC Gibbs sampling Probit model is adopted to maintain the three advantages it has in dealing with heteroscedasticity, high dimension numerical integral and sample size restriction problems. The results revealed that the MCMC model and MLE Probit model are strikingly consistent, which suggests that the former is much simple and reliable estimation method. At the same time, the empirically based existence value estimation of coastal beach quality improvement in Dalian, China is RMB?168 per person.

基于MCMC的DOA估计及其在空域滤波中的应用

针对传统算法在进行波达方向角估计时需要进行全空间的谱峰搜索,且依赖初始化迭代和插值的缺陷,设计了一种贝叶斯估计方法。首先,构建基于均匀圆阵的频域信号处理模型,提出了基于马尔科夫链蒙特卡罗进行波达方向角估计的方法,实现了无需初始化和插值的测向算法,且不需要进行全空间的谱峰搜索即可直接估计结果,同时推导了该模型下进行波达方向角估计的克拉美罗下界。在以测向结果为先验信息,同时设计了一种新的空域滤波增益模型。利用马尔科夫链蒙特卡罗算法对信号子空间进行解耦合,然后计算滤波器增益系数,以此实现空域滤波。最后在无线电设备搭建的真实环境中进行实验,验证了基于马尔科夫链蒙特卡罗算法进行阵列波达方向角估计和空域滤波的有效性,并且与其他模型相比要明显优于后者。

基于不同MCMC抽样子集模拟的模型修正方法对比研究

子集模拟方法作为结构可靠度分析方法,也可应用于工程优化问题,诸如优化设计、模型修正等.为研究基于不同蒙特卡洛马尔可夫链(MonteCarloMarkovChain,MCMC)抽样的子集模拟优化方法(Subset Simulation Optimization,SSO),以有限元模型修正作为优化背景问题,开展其精度和效率的对比研究.介绍标准SSO和子集模拟(Subset Simulation,SS)常见的MCMC抽样方法,并基于上述不同MCMC抽样的SSO开展某局部损伤悬臂梁(10维变量)的有限元模型修正,修正结果与基于遗传算法(Genetic Algorithm,GA)的模型修正方法进行对比;而后将上述不同MCMC抽样的SSO修正方法应用于某四层钢框架有限元模型修正中(11维变量).结果表明,采用随机游走的延迟拒绝修正M-H方法(MMH algorithm with Delayed Rejection,MMHDR)和自适应条件抽样方法(Adaptive Conditional Sampling,ACS)的SSO有限元模型修正具有较好的精度和效率,在工程结构有限元模型修正中更具优势.

Dynamic Spatio-Temporal Modeling in Disease Mapping

Spatio-temporal models are valuable tools for disease mapping and understanding the geographical distribution of diseases and temporal dynamics. Spatio-temporal models have been proven empirically to be very complex and this complexity has led many to oversimply and model the spatial and temporal dependencies independently. Unlike common practice, this study formulated a new spatio-temporal model in a Bayesian hierarchical framework that accounts for spatial and temporal dependencies jointly. The spatial and temporal dependencies were dynamically modelled via the matern exponential covariance function. The temporal aspect was captured by the parameters of the exponential with a first-order autoregressive structure. Inferences about the parameters were obtained via Markov Chain Monte Carlo (MCMC) techniques and the spatio-temporal maps were obtained by mapping stable posterior means from the specific location and time from the best model that includes the significant risk factors. The model formulated was fitted to both simulation data and Kenya meningitis incidence data from 2013 to 2019 along with two covariates;Gross County Product (GCP) and average rainfall. The study found that both average rainfall and GCP had a significant positive association with meningitis occurrence. Also, regarding geographical distribution, the spatio-temporal maps showed that meningitis is not evenly distributed across the country as some counties reported a high number of cases compared with other counties.

基于贝叶斯方法的软土深基坑不确定性位移反演分析

在深基坑开挖工程中,使用合理的土体力学参数计算地连墙侧移对优化基坑支护方案以及降低工程风险至关重要。然而,受地层分布不均、土工试验误差等因素的影响,土体参数常表现出明显的不确定性,该不确定性降低了地连墙侧移计算结果的可信度。鉴于上述问题,提出一种基于贝叶斯参数更新框架和现场监测数据的软土深基坑土体参数反演方法。该方法采用GA-BP神经网络建立数值分析模型中土体参数与地连墙侧移的隐式函数关系,并结合现场监测数据建立土体参数的贝叶斯反分析模型。采用该方法对某深基坑工程进行分析,验证了方法的可行性。分别采用地连墙的最大侧移值和多点位移值作为指标进行土体力学参数反演,并分别使用更新后的土体参数预测基坑开挖的最终侧移值。研究结果表明:与不更新土体参数相比,更新土体参数后土体参数变异系数变小,得到的结果与后续施工步下的监测结果更吻合;使用多点观测值进行土体参数更新的预测效果显著优于仅使用最大位移值时的效果。

基于NEATM和WISE数据的小尺寸近地小行星物理特性研究

小行星是太阳系中广泛分布的金属或岩石天体,直径从米级跨越到几百公里.它们蕴含了太阳系早期的信息,同时也可能会与地球轨道相交且撞击地球,因此研究小行星的物理参数、物质成分和表面性质对于了解太阳系行星的形成演化和近地天体防御具有重要意义.以国际小行星中心(Minor Planet Center,MPC)获取直径D<160 m的小尺寸近地小行星共67颗作为研究对象,其中包含部分潜在威胁小行星(Potentially Hazardous Asteroids,PHA).基于NEATM(Near-Earth Asteroid Thermal Model),使用广域红外巡天望远镜(Wide-field Infrared Survey Explorer,WISE)的观测数据,利用反射光模型对太阳反射光进行了修正,使用动力学模型计算WISE观测历元的小行星轨道数据,计算了这67颗小尺寸近地小行星的直径和反照率.拟合过程采用马尔科夫链蒙特卡洛(Markov Chain Monte Carlo,MCMC)方法,与WISE的研究结果和MPC的数据进行了比较分析,给出了其分类特征.研究为小行星的观测和理论提供了有力的支持,可以更好地了解近地小行星的特征和演化.