Objective Bayesian analysis of the Frechet stress–strength model

Several reference priors and a general form of matching priors are derived for a stress–strength system, and it is concluded that none of the reference priors is a matching prior. The study shows that the matching prior performs better than Jeffreys prior and reference priors in meeting the target coverage probabilities.     

基于贝叶斯方法的Meta-分析

本文基于贝叶斯Meta-分析给出了Jeffreys无信息先验B(1/2,1/2)时的链接分布,并将其进行推广,给出了先验分布为B(α,β)时的链接分布。     

Bayesian Analysis of Exafs Data

In this paper we demonstrate the use of Bayesian analysis methods for the analysis of EXAFS data. We will discuss the physical parameters that may be estimated by the method and demonstrate the applicability of the method to Molybdenum coordination compounds.     

Dynamic analysis and regulation of the flexible pipe conveying fluid with a hard-magnetic soft segment

Applied Mathematics and Mechanics - The recently developed hard-magnetic soft (HMS) materials can play a significant role in the actuation and control of medical devices, soft robots, flexible...     

Bayesian Estimation and Statistical Analysis of Risk Measurements

The Bayesian model are established for the VaR and related risk measurements. The relationship between VaR and other risk measurements including expect shortfall, tail condition expectation and conditional value at risk are discussed. Furthermore, the Bayesian estimates and Bayesian predictors of these risk measurement are derived. Thirdly, the consistency and asymptotic normality in the exponential risk model are proved. Finally, the numerical simulation method is used to verify the convergence rate under different sample sizes.     

Recent advances of organometallic complexes in emerging photovoltaics

Organometallic complexes (OMCs) consisting of organic and metal active moieties have shown immense potential for application in solar cells. The diverse structure, rich porosity, and unique charge centers of OMCs enable them to be functional in solar cells. In this review, we introduced four types of OMCs, such as crown organometallic complexes, β-diketone metal complexes, cyclometallic complexes, and main chain metal-containing polymers, providing an in-depth analysis of the structure-performance relationship. OMCs could serve as active or interlayer materials in a variety of solar cell systems such as organic solar cells, perovskite solar cells, and dye-sensitized solar cells, especially some metals to improve the photoelectric performance of the device as dopants. In the end, perspectives on the opportunities and challenges of OMCs are given.     

基于近似贝叶斯计算方法的排队模型参数估计

基于贝叶斯推断对单服务台的马尔科夫与非马尔科夫系统下的4个排队模型做参数估计与效果评价,并对银行排队叫号换汇数据做实证研究。采用近似贝叶斯计算的方法有效解决复杂排队模型似然函数难解析表达的问题。利用R中queue computer包给出M/M/1,M/G/1,G/M/1,GI/GI/1这4个排队模型的到达时间与服务时间的参数估计及后验分布图。其中M/M/1:估计值(真实值)分别约为1.02(1)、 1.12(1/0.9),都与真实值接近;M/G/1:1.17(1)、1.21(1.2),对服务时间的估计效果优于到达时间;G/M/1:0.49(0.5)、0.96(1),从后验分布进一步得到对服务时间的估计更准确;GI/GI/1:1.16(1.1)、1.07(1)、0.23(0.251)、0.22(0.25),各分量的估计值与真实值相对接近。对于实际银行数据,估计得的参数所拟合的数据与Ausin的拟合数据分布接近。研究表明,近似贝叶斯的方法在排队模型的参数估计上有较大优势,在实际数据中取得了较好应用。     

Bayesian selection of graphical regulatory models

We define a new class of coloured graphical models, called regulatory graphs. These graphs have their own distinctive formal semantics and can directly represent typical qualitative hypotheses about regulatory processes like those described by various biological mechanisms. They admit an embellishment into classes of probabilistic statistical models and so standard Bayesian methods of model selection can be used to choose promising candidate explanations of regulation. Regulation is modelled by the existence of a deterministic relationship between the longitudinal series of observations labelled by the receiving vertex and the donating one. This class contains longitudinal cluster models as a degenerate graph. Edge colours directly distinguish important features of the mechanism like inhibition and excitation and graphs are often cyclic. With appropriate distributional assumptions, because the regulatory relationships map onto each other through a group structure, it is possible to define a conditional conjugate analysis. This means that even when the model space is huge it is nevertheless feasible, using a Bayesian MAP search, to a discover regulatory network with a high Bayes Factor score. We also show that, like the class of Bayesian Networks, regulatory graphs also admit a formal but distinctive causal algebra. The topology of the graph then represents collections of hypotheses about the predicted effect of controlling the process by tearing out message passers or forcing them to transmit certain signals. We illustrate our methods on a microarray experiment measuring the expression of thousands of genes as a longitudinal series where the scientific interest lies in the circadian regulation of these plants.     

Learning extended tree augmented naive structures

This work proposes an extended version of the well-known tree-augmented naive Bayes (TAN) classifier where the structure learning step is performed without requiring features to be connected to the class. Based on a modification of Edmonds' algorithm, our structure learning procedure explores a superset of the structures that are considered by TAN, yet achieves global optimality of the learning score function in a very efficient way (quadratic in the number of features, the same complexity as learning TANs). We enhance our procedure with a new score function that only takes into account arcs that are relevant to predict the class, as well as an optimization over the equivalent sample size during learning. These ideas may be useful for structure learning of Bayesian networks in general. A range of experiments shows that we obtain models with better prediction accuracy than naive Bayes and TAN, and comparable to the accuracy of the state-of-the-art classifier averaged one-dependence estimator (AODE). We release our implementation of ETAN so that it can be easily installed and run within Weka.