Building prior distributions to support Bayesian reliability growth modelling using expert judgement

A sound methodology for the elicitation of subjective expert judgement is a pre-requisite for specifying prior distributions for the parameters of reliability growth models. In this paper, we describe an elicitation process that is developed to ensure valid data are collected by suggesting how possible bias might be identified and managed. As well as discussing the theory underpinning the elicitation process, the paper gives practical guidance concerning its implementation during reliability growth testing. The collection of subjective data using the proposed elicitation process is embedded within a Bayesian reliability growth modelling framework and reflections upon its practical use are described.     

Full-text prior art and chemical structure searching in e-journals and on the internet – A patent information professional’s perspective

In depth analysis of non-patent literature prior art is a crucial step in checking patentability of new inventions and validity of competitor’s patents, since by patent law relevant subject matter disclosed in non-patent literature is as important as any patent document. E-journal articles, as well as any scientific and technical information published on the web are an important source of prior art that is very often insufficiently covered and indexed by commercial databases. This article reviews search and display capabilities of e-journal search sites of different publishers and hosts, as well as their value for full-text prior art analysis to enhance retrieval from commercial databases. Moreover, current developments and future prospects of chemical structure searching both in e-journals and on the internet are discussed.     

基于场景先验知识的室内人体行为识别方法

目前,室内人体行为识别技术被广泛应用于视频内容理解、居家养老、医疗护理等领域,现有研究方法更多的是对人体行为进行建模,忽略了视频中场景与人体行为间的联系。为了充分利用场景信息与室内人体运动的关联性,文中对基于场景先验知识的室内人体行为识别方法进行了研究,提出了一种基于场景先验知识的双流膨胀3D行为识别网络(Scene-Prior Know-ledge Inflated 3D ConvNet, SPI3D)。首先使用ResNet152网络提取场景特征进行场景分类,再基于场景分类的结果,引入量化后的场景先验知识,通过对权值进行约束来优化总体目标函数。另外,针对现有数据集多聚焦于人体行为特征、场景复杂且场景特征不明显的问题,自建了室内场景-行为识别数据集(Scene-Action DataBase, SADB)。实验结果表明,在SADB数据集上,SPI3D网络的识别准确率为87.9%,比直接利用I3D网络的识别准确率高6%。由此可见,引入场景先验知识后的室内人体行为识别模型具有更好的表现。     

一种基于先验信息的混合数据聚类个数确定算法

聚类个数的确定是聚类分析中一个富有挑战性的难题。现有的聚类个数确定方法主要采用随机选取初始聚类中心的策略,导致聚类过程中迭代次数的稳定性不强。基于此,在利用含有类标签的先验信息优化初始类中心的基础上,提出了一种基于先验信息的混合数据聚类个数确定算法。实验证明,该算法是有效的。     

融合先验信息的非负矩阵分解社区发现算法*

针对复杂网络社区发现问题,为了获得更准确、可解释性的社区划分结果,提出融合先验信息的半监督非负矩阵分解算法,给出优化目标的求解方法.文中算法利用先验信息直接约束社区指示矩阵,构造优化目标函数,获得更有意义的社区划分结果.真实数据集上的实验表明该算法的有效性,减小先验信息的融入对利用非负矩阵分解进行节点重要性等属性分析工作带来的不利影响,并且适用于加权和非加权等不同的网络.     

Numerical maximum log likelihood estimation for generalized lambda distributions

This paper presents a two-step procedure using the method of moment or percentile to find initial values and then maximize the numerical log likelihood to fit the appropriate generalized lambda distribution to data. This paper demonstrates the use of this procedure to fit well-known statistical distributions as well as some empirical data. Overall, the use of numerical maximum log likelihood estimation is a valuable alternative among existing methods of fitting. It provides not only convincing results in terms of quantile plots and goodness of fit tests but also has the advantage of a lower variability in its parameter estimation compared to the existing starship (King and MacGillivray, 1999) and method of moment (Karian and Dudewicz, 2000) fitting schemes.     

Information integration for complex systems

This paper develops a framework to determine the performance or reliability of a complex system. We consider a case study in missile reliability that focuses on the assessment of a high fidelity launch vehicle intended to emulate a ballistic missile threat. In particular, we address the case of how to make a system assessment when there are limited full-system tests. We address the development of a system model and the integration of a variety of data using a Bayesian network.     

Bayesian estimation of a normal mean parameter using the Linex loss function and robustness considerations

Summary This paper presents a derivation of an explicit analytical form for the Bayes estimator of the normal location parameter using the Linex loss function with a general class of prior distributions. Exact and approximate results based on Pericchi and Smith’s paper (1992) are given, where the priors are double-exponential and Studentt, respectively. The results of this paper provide a link between the robust Bayesian analysis for the normal location parameter when adopting either the Linex loss function or the squared error loss function.     

Predictive efficiency of improved estimators in restricted regression models

Summary This paper considers the predictive efficiency of some improved estimators when there are exact prior restrictions on the coefficients of the linear regression model. A comparative study of various improved estimators in terms of their respective risks is also described.     

Probability matching priors for linear calibration

Summary In the linear calibration problem, a model is fit to paired observations arising from two measurement techniques, one known to be far more accurate (but also more expensive) than the other. The fitted model is then used with univariate observations from the less accurate technique to impute values from the more accurate one. The Bayesian paradigm emerges as attractive in this context, but the choice of an appropriate noninformative prior distribution has been controversial. In this paper we derive a class of such distributions, and provide sufficient conditions under which they lead to proper posterior densities. These priors, which we refer to asprobability matching priors, are designed to produce posterior credible intervals which are asymptotically identical to their frequentist counterparts. We provide details on the implementation of our procedure using sampling-based methods, and obtain significant simplifications over previous Bayesian approaches in this area. We compare the performance of several members of our prior class in the context of two illustrative examples.