Likelihood-based estimation of continuous-time epidemic models from time-series data: application to measles transmission in London

We present a new statistical approach to analyse epidemic time-series data. A major difficulty for inference is that (i) the latent transmission process is partially observed and (ii) observed quantities are further aggregated temporally. We develop a data augmentation strategy to tackle these problems and introduce a diffusion process that mimics the susceptible-infectious-removed (SIR) epidemic process, but that is more tractable analytically. While methods based on discrete-time models require epidemic and data collection processes to have similar time scales, our approach, based on a continuous-time model, is free of such constraint. Using simulated data, we found that all parameters of the SIR model, including the generation time, were estimated accurately if the observation interval was less than 2.5 times the generation time of the disease. Previous discrete-time TSIR models have been unable to estimate generation times, given that they assume the generation time is equal to the observation interval. However, we were unable to estimate the generation time of measles accurately from historical data. This indicates that simple models assuming homogenous mixing (even with age structure) of the type which are standard in mathematical epidemiology miss key features of epidemics in large populations.     

Bayesian Probabilistic Inference for Nonparametric Damage Detection of Structures

This paper presents a Bayesian hypothesis testing-based probabilistic assessment method for nonparametric damage detection of building structures, considering the uncertainties in both experimental results and model prediction. A dynamic fuzzy wavelet neural network method is employed as a nonparametric system identification model to predict the structural responses for damage evaluation. A Bayes factor evaluation metric is derived based on Bayes’ theorem and Gaussian distribution assumption of the difference between the experimental data and model prediction. The metric provides quantitative measure for assessing the accuracy of system identification and the state of global health of structures. The probability density function of the Bayes factor is constructed using the statistics of the difference of response quantities and Monte Carlo simulation technique to address the uncertainties in both experimental data and model prediction. The methodology is investigated with five damage scenarios of a four-story benchmark building. Numerical results demonstrate that the proposed methodology provides an effective approach for quantifying the damage confidence in the structural condition assessment.     

Neurotoxic lesions of retrosplenial cortex disrupt signaled and unsignaled contextual fear conditioning.

The majority of research regarding contextual learning and memory has focused on the contributions of the hippocampus and related medial temporal lobe structures. However, little is known about other possible cortical contributions to these processes. Our laboratory recently demonstrated that electrolytic lesions of the retrosplenial cortex (RSP), a posterior region of cingulate cortex, impaired contextual but not cue-specific fear conditioning. The present experiments further examined the role of RSP in contextual fear memory using fiber-sparing neurotoxic lesions and both signaled and unsignaled fear conditioning paradigms. Despite comparable acquisition of the conditioned fear response, rats with neurotoxic lesions of RSP exhibited impaired contextual memory relative to control animals in both the signaled and unsignaled paradigms. These results further suggest an important role for RSP in contextual learning and memory. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

基于贝叶斯网络的电网复杂故障推演新方法

为了分析电网复杂故障过程,基于保护装置与断路器的动作信息构建贝叶斯网络,提出了电网复杂故障推演新方法。应用电网实际拓扑结构信息、继电保护装置和断路器的动作信息构建贝叶斯网络。基于贝叶斯网络进行故障元件诊断,针对远后备保护、近后备保护误判的两种特殊情况,制定了专家系统规则实现保护拒动、误动的识别。将保信系统上送信息与拒动误动分析结果按主保护层-近后备保护-远后备保护-断路器层顺序进行信息整合,推演出发生故障时各层保护装置以及断路器动作先后顺序,实现了电网复杂故障推演。     

Hadoop下基于贝叶斯网络的气象数据挖掘研究

基于朴素贝叶斯的分类器是气象数据挖掘中比较传统的方法,但由于算法要求各属性相互独立,预测精度无法达到要求,且在处理海量数据时算法计算效率受到制约,对此提出一种Hadoop平台下基于离散贝叶斯网络的数据挖掘改进算法。算法不要求属性之间相互独立,且充分结合Hadoop平台适应处理大数据的优点,利用海量数据分析地面气象因素与温度之间的相关性,并由此选取预测因子来训练贝叶斯网络分类器模型,以达到预测温度的目的。实验结果表明,算法不但预测精度明显高于目前短期气候预测中采用的朴素贝叶斯算法,而且极大地提高了运算效率。     

改进型ICA和SVM相结合的火山灰云遥感检测

针对独立分量分析(ICA)模型在火山灰云遥感检测中的不足,提出了一种改进型ICA即变分贝叶斯ICA (VBICA)和支持向量机(SVM)相结合的火山灰云遥感检测算法,实现了火山灰云信息的近似分离.实验结果表明,所提算法能够从中分辨率成像光谱仪(MODIS)遥感图像中检测出火山灰云目标信息,且总检测精度和Kappa系数分别达到了88.4％和0.801 1,取得了较好的检测效果.     

高速公路车辆行驶路径模糊匹配分析

高速公路车辆行驶路径在省界站处被自然分隔为两段,不能真实反映行驶车辆在高速公路上的路径信息.本文选取收费系统中车牌号、出入口时间、轴型、车货总重等字段信息,利用贝叶斯方法对分隔路径进行模糊匹配,从而得出行驶车辆在高速公路的全路径信息.     

高斯粒子流滤波器

粒子流滤波器以粒子流速度场描述随机样本从先验分布到后验分布的演化,实现对系统状态的贝叶斯估计.针对其一般解计算复杂、难于滤波求解的问题,导出一种高斯假设条件下的粒子流滤波器.在线性高斯条件下推导了速度场的解析解;证明了当演化步长趋近于0时,该解析解与Kalman-Bucy滤波器的解具有一致的形式;基于该解导出了非线性高斯系统速度场的表达式,并进一步利用Unscented变换近似求解.通过若干仿真算例表明,高斯粒子流滤波器放宽了系统噪声为高斯型的限制,其精度优于经典非线性高斯滤波器,计算复杂度低于一般粒子滤波器,且具有良好的稳定性.     

利用贝叶斯判别法识别岩性基础上的孔隙度评价

伊拉克M油田具有岩性复杂、储集空间多样、非均质性强等特征,常规孔隙度计算模型不能较好地反映其储层孔隙度。为了提高其孔隙度计算精度,在计算孔隙度前先对储层作了岩性判别。以最小错误率的贝叶斯判别法为基础,选取研究区测井资料齐全和岩性分布较均衡的5口井作为样本,选择合适的测井曲线作为输入曲线,利用贝叶斯公式求得每个深度点的各类后验概率值,后验概率值最大的即为该点所属的类别,以此为依据对所有井作了岩性识别。在岩性识别的基础上对经过岩心分析的岩样分类拟合以获得各类岩性的拟合模型以及在该地区的骨架值,根据所输出的岩性代码,可选择该深度点所对应的测井解释模型。识别岩性后计算的孔隙度与岩心分析孔隙度的误差很小,为储层的解释评价提供了理论基础。     

基于攻防信号博弈模型的防御策略选取方法

当前基于博弈理论的防御策略选取方法大多采用完全信息或静态博弈模型,为更加符合网络攻防实际,从动态对抗和有限信息的视角对攻防行为进行研究。构建攻防信号博弈模型,对策略量化计算方法进行改进,并提出精炼贝叶斯均衡求解算法。在博弈均衡分析的基础上,设计了最优防御策略选取算法。通过实验验证了模型和算法的有效性,并在分析实验数据的基础上总结了攻防信号博弈的一般性规律,能够指导不同类型防御者的决策。