Use of kernel-based Bayesian models to predict late osteolysis after hip replacement

We studied the relationship between osteolysis and polyethylene wear, age at surgery, body mass index and height in 463 subjects (180 osteolysis and 283 controls) after cemented Charnley total hip arthroplasty (THA), in order to develop a kernel-based Bayesian model to quantitate risk of osteolysis. Such tools may be integrated into decision-making algorithms to help personalize clinical decision-making. A predictive model was constructed, and the estimated posterior probability of the implant failure calculated. Annual wear provided the greatest discriminatory information. Age at surgery provided additional predictive information and was added to the model. Body mass index and height did not contain valuable discriminatory information over the range in which observations were densely sampled. The robustness and misclassification rate of the predictive model was evaluated by a five-times cross-validation method. This yielded a 70% correct predictive classification of subjects into osteolysis versus non-osteolysis groups at a mean of 11 years after THA. Finally, the data were divided into male and female subsets to further explore the relationship between wear rate, age at surgery and incidence of osteolysis. The correct classification rate using age and wear rate in the model was approximately 66% for males and 74% for females.     

Multivariate spatial models of excess crash frequency at area level: Case of Costa Rica

Recently, areal models of crash frequency have being used in the analysis of various area-wide factors affecting road crashes. On the other hand, disease mapping methods are commonly used in epidemiology to assess the relative risk of the population at different spatial units. A natural next step is to combine these two approaches to estimate the excess crash frequency at area level as a measure of absolute crash risk. Furthermore, multivariate spatial models of crash severity are explored in order to account for both frequency and severity of crashes and control for the spatial correlation frequently found in crash data.     

Bayes Linear Estimators

A class of linear estimators, called Bayes linear estimators, is developed by finding, among all linear estimators, ones which have least average total mean squared error, averaged over parameter points. Ridge, generalized ridge, restricted least squares, subset least squares, least squares, best, and generalized inverse linear estimators are all either Bayes linear estimators or limits of Bayes linear estimators. Results on Bayes linear estimators are extended to affine estimators. “Bootstrapping” procedures, in which the data are recycled in the guise of prior information, are discussed.     

Clean energy investment scenarios using the Bayesian network

Clean energy investment decisions are getting more difficult to make due to public reactions. In order to support the policies in the field, analysis of the positive conditions is needed. This research aims to construct the positive scenarios for nuclear energy and renewable energy investments in the state of Oregon, USA. The Bayesian network technique will be used to create the scenarios. Oregon has a wide range of renewable energies; hence, investment is becoming more complex. Criteria affecting the decisions are taken from the literature, but were reviewed with energy authorities in Oregon in order to define the interactions.     

Churn prediction using comprehensible support vector machine: An analytical CRM application

Support vector machine (SVM) is currently state-of-the-art for classification tasks due to its ability to model nonlinearities. However, the main drawback of SVM is that it generates “black box” model, i.e. it does not reveal the knowledge learnt during training in human comprehensible form. The process of converting such opaque models into a transparent model is often regarded as rule extraction. In this paper we proposed a hybrid approach for extracting rules from SVM for customer relationship management (CRM) purposes. The proposed hybrid approach consists of three phases. (i) During first phase; SVM-RFE (SVM-recursive feature elimination) is employed to reduce the feature set. (ii) Dataset with reduced features is then used in the second phase to obtain SVM model and support vectors are extracted. (iii) Rules are then generated using Naive Bayes Tree (NBTree) in the final phase. The dataset analyzed in this research study is about Churn prediction in bank credit card customer (Business Intelligence Cup 2004) and it is highly unbalanced with 93.24% loyal and 6.76% churned customers. Further we employed various standard balancing approaches to balance the data and extracted rules. It is observed from the empirical results that the proposed hybrid outperformed all other techniques tested. As the reduced feature dataset is used, it is also observed that the proposed approach extracts smaller length rules, thereby improving the comprehensibility of the system. The generated rules act as an early warning expert system to the bank management.     

Stability of Karhunen–Loève expansion for the simulation of Gaussian stochastic fields using Galerkin scheme

The paper investigates the problem of numerical stability of the Karhunen–Loève expansion for the simulation of Gaussian stochastic fields using Galerkin scheme. The instability is expressed as loss of positive definiteness of covariance matrix and is the result of modifications of standard exponential covariance functions that are commonly applied to increase the sparsity of the covariance matrix. The loss of positive definiteness of covariance matrix limits the use of efficient eigenvalue solvers that are needed for the solution of the resulting generalized eigenvalue problem. Two modifications of the shape of covariance function to avoid instability problems and at the same time to raise the numerical efficiency of Karhunen–Loève expansion by increasing the sparsity of the covariance matrix are proposed. The effects of the proposed modifications are demonstrated on numerical examples.     

Identification of Novel Pathways in Plant Lectin-Induced Cancer Cell Apoptosis

Plant lectins have been investigated to elucidate their complicated mechanisms due to their remarkable anticancer activities. Although plant lectins seems promising as a potential anticancer agent for further preclinical and clinical uses, further research is still urgently needed and should include more focus on molecular mechanisms. Herein, a Naïve Bayesian model was developed to predict the protein-protein interaction (PPI), and thus construct the global human PPI network. Moreover, multiple sources of biological data, such as smallest shared biological process (SSBP), domain-domain interaction (DDI), gene co-expression profiles and cross-species interolog mapping were integrated to build the core apoptotic PPI network. In addition, we further modified it into a plant lectin-induced apoptotic cell death context. Then, we identified 22 apoptotic hub proteins in mesothelioma cells according to their different microarray expressions. Subsequently, we used combinational methods to predict microRNAs (miRNAs) which could negatively regulate the abovementioned hub proteins. Together, we demonstrated the ability of our Naïve Bayesian model-based network for identifying novel plant lectin-treated cancer cell apoptotic pathways. These findings may provide new clues concerning plant lectins as potential apoptotic inducers for cancer drug discovery.     

基于贝叶斯动态模型的某器件性能预测

在导弹武器系统当中,及时准确的故障预报对提高导弹的安全性具有极其重要的意义。本文根据导弹惯性器件故障预报系统的设计要求,利用贝叶斯动态线性模型对导弹某惯性器件的性能进行了预测研究。贝叶斯预测是利用历史信息和从样本获得的信息来获取后验分布的一种预测方法,该方法不需要平稳性的假设,而且充分利用了已有的信息,可以用来对电子设备的性能进行预测。实例研究表明,该方法具有比较好的预测效果。     

基于QDCT和贝叶斯谱熵的盲彩色图像质量评价

图像质量评价算法在评价彩色图像质量时,往往会因损失色彩信息或者破坏彩色图像结构的整体性,而使得评价结果与人眼观测结果不一致.由于图像越模糊其频谱的高频分量分布越不均匀,基于四元数离散余弦变换(QDCT)和贝叶斯谱熵,提出了一种无参考模糊彩色图像质量评价算法.首先,利用四元数矩阵对彩色图像进行表示并分解成不重叠的8×8 ...     

离散贝叶斯分类算法雷达目标一维距离像识别

首先讨论了传统贝叶斯分类器设计方法的局限性,然后提出了离散贝叶斯分类算法,并推导了离散贝叶斯分类器的分类误差估算公武.用大量的实测高分辨雷达目标回波数据进行计算机模拟,离散贝叶斯分类算法获得了平均94.33%的正确识别率,分类误差估算公式估算的分类误差为5.23%.计算机模拟结果表明了该方法的有效性.