Bayesian inference for a partially observed birth–death process using data on proportions

Stochastic kinetic models are often used to describe complex biological processes. Typically these models are analytically intractable and have unknown parameters which need to be estimated from observed data. Ideally we would have measurements on all interacting chemical species in the process, observed continuously in time. However, in practice, measurements are taken only at a relatively few time‐points. In some situations, only very limited observation of the process is available, for example settings in which experimenters can only observe noisy observations on the proportion of cells that are alive. This makes the inference task even more problematic. We consider a range of data‐poor scenarios and investigate the performance of various computationally intensive Bayesian algorithms in determining the posterior distribution using data on proportions from a simple birth‐death process.     

Analytical expression for the integrated squared density partial derivative of a multivariate normal mixture distribution

This paper describes the derivation of the analytical expression for the integrated squared density partial derivative (ISDPD) in a multivariate normal mixture model. The analytical expression of the ISDPD is derived for arbitrary dimensions with partial derivative orders up to four. Although the value of the ISDPD can be obtained by using the common numerical integration via mathematical software (such as Maple, Mathematica, Matlab, etc), it suffers from the limitation of computation time when the dimension or the number of mixture components of the considered multivariate normal mixture model are large. Moreover, numerical comparison indicates the benefits of speed offered by our proposed analytical expression are far superior to the numerical integration performed by Maple. With this analytical expression, the ISDPD can apace be calculated with no assistance of numerical integration.     

论网络语言中谐音的运用

网络语言是网络时代出现的一种新型语篇类型,是语言的一种功能变体,其标志性语体特征是变异性和个性化.而许多网络词语和语句正是运用了谐音的手段,使网络语言呈现出其特有的魅力.最后,文章对网络语言的发展和规范化问题进行了探讨.     

广义Gamma分布簇广义线性混合模型的构建

 本文在广义Gamma分布簇基础上引入异质性来构建广义线性混合模型。本文构建的广义Gamma分布簇广义线性混合模型在广义线性混合模型的框架下分析,通过参数重整技术把广义Gamma分布簇变量的建模问题与指数分布簇变量的建模问题联系起来,模型推断可以方便地利用广义线性混合模型和广义线性模型的研究成果,同时也可以方便地推广到其他模型。三参数广义Gamma分布可以收缩到两参数的Gamma分布、Weibull分布或指数分布,能降低模型误设的风险,还能便利地分析误差结构。     

基于PSO-PLS的组合预测方法在GDP预测中的应用

GDP预测是经济预测中一个非常重要的问题,随着经济的发展,对其预测精度的要求也越来越高.在考虑样本权重的基础上,提出一种微粒群算法与部分最小二乘回归方法相结合的组合预测方法,即采用微粒群方法对样本最优权重进行求解,在所得样本权重系数的基础上,用部分最小二乘回归方法确定组合预测的权重系数.将该方法用于中国GDP预测取得了较好的结果,与其他几种传统方法相比,预测精度有一定程度的提高,说明算法的有效性和可行性.     

部分W-分离正交语音信号的盲分离方法

在W-分离正交性假设的语音盲分离方法中,由于没有考虑多个源信号同时存在的情况,导致分离信号中不可避免地存在音乐噪声。针对这种部分W-分离正交情况,提出了基于信道估计的语音盲分离方法。该方法先检测只有一个源信号存在的时频点并进行归一化处理,使得处理后的结果与频率无关,克服了W-分离正交性假设的不足以及频率置换问题,通过K-means聚类估计出信道,再结合信号子空间方法重构源信号。仿真结果表明,提出的方法可以有效减少分离语音中的音乐噪声,与典型的时频二元掩蔽方法相比,其平均信号失真比提高3.02dB,同时平均信干比提高4.61dB。     

一阶偏微分方程的特征线法

介绍了一阶偏微分方程特征线法的由来、几何意义及求解的基本思想.     

我国上市金融公司资本结构影响因素分析

运用偏最小二乘回归方法,采用我国上市金融公司2002年的财务数据,对影响我国上市金融公司资本结构的10个因素进行实证分析。结果表明:10个因素对金融公司与实业公司资本结构的影响有着明显区别;政府对金融发展的控制有倾向性;金融市场行为也体现出更加注重金融公司的风险性。     

从局部入手画单个物体--探索平面设计教育中的素描教学

目前,我国的设计院校对入学新生进行的素描训练大都采用两种训练方式:一种是沿袭以前的"苏式"静物写生训练,另一种是结构素描形式的写生训练.这两种训练方式在面对学生实际和素描服务于平面设计的要求上,都有一些不足和局限.在实践中探索出一个有别于传统素描教学模式的训练方案--从局部入手画单个的物体.