Recovering Linear Operators from Inaccurate Data

This paper deals with approximating linear operators from information contaminated with bounded noise. We derive some upper and lower estimates on the diameter of inaccurate information and compare it to that of exact information. We also point out conditions under which the bounds coincide and yield the diameter exactly.     

求解分段常数图象分割模型的一个快速算法

针对Xue-ChengTai等提出的分段常数图象分割模型,我们提出了一个新的快速求解算法。通过引进一个函数来选择模型中的正则化参数β的值,并判断在迭代过程中何时求解不含惩罚项的泛函F。此函数的引入有效地加速了算法的收敛速度。结合原始-对偶Newton方法来求解总变差最小化问题。数值试验表明新算法具有很快的收敛速度与良好的分割效果,且算法对初始值的要求不高。     

Global Optimal Image Reconstruction from Blurred Noisy Data by a Bayesian Approach

In this paper, a procedure is presented which allows the optimal reconstruction of images from blurred noisy data. The procedure relies on a general Bayesian approach, which makes proper use of all the available information. Special attention is devoted to the informative content of the edges; thus, a preprocessing phase is included, with the aim of estimating the jump sizes in the gray level. The optimization phase follows; existence and uniqueness of the solution is secured. The procedure is tested against simple simulated data and real data.     

Data Dependent Triangulations for Piecewise Linear Interpolation

Given a set of data points in R² and corresponding data values,it is clear that the quality of a piecewise linear interpolationover triangles depends on the specific triangulation of thedata points. While conventional triangulation methods dependonly on the distribution of the data points in R² in this paperwe suggest that the triangulation should depend on the datavalues as well. Several data dependent criteria for definingthe triangulation are discussed and efficient algorithms forcomputing these triangulations are presented. It is shown fora variety of test cases that data dependent triangulations canimprove significantly the quality of approximation and thatlong and thin triangles, which are traditionally avoided, aresometimes very suitable.     

Recovering a Time-Dependent Diffusion Coefficient from Nonlocal Data

In this paper, an inverse problem of recovering a leading time-dependent coefficient from nonlocal additional information is investigated. To approximately solve the nonlinear inverse problems, we propose a gradient method of minimizing an objective functional. A comparative analysis with a method based on a linearized approximation scheme with respect to time is performed. The results of numerical calculations are presented.     

分片常系数电导率问题的边界元配点法

用边界元方法讨论了具有分片常系数电导率方程Δ↓(γΔ↓u)=0的Dirichlet边值问题,由于方程的基本解无法显式写出,在应用通常边界元时存在很大的困难,基于这个电导率方程的解的积分表达式,导出一个在边界和交界面上的积分方程组,并讨论了这个方程组的性质,对于这个积分方程组,用配点法进行求解,且给出其误差分析．相应的数值例子证实了算法的有效性．应该指出的是本文所用的方法也适用于具有分片常系数椭圆方程的不同边界问题。     

Covariance Partition Priors: A Bayesian Approach to Simultaneous Covariance Estimation for Longitudinal Data

The estimation of the covariance matrix is a key concern in the analysis of longitudinal data. When data consist of multiple groups, it is often assumed the covariance matrices are either equal across groups or are completely distinct. We seek methodology to allow borrowing of strength across potentially similar groups to improve estimation. To that end, we introduce a covariance partition prior that proposes a partition of the groups at each measurement time. Groups in the same set of the partition share dependence parameters for the distribution of the current measurement given the preceding ones, and the sequence of partitions is modeled as a Markov chain to encourage similar structure at nearby measurement times. This approach additionally encourages a lower-dimensional structure of the covariance matrices by shrinking the parameters of the Cholesky decomposition toward zero. We demonstrate the performance of our model through two simulation studies and the analysis of data from a depression study. This article includes Supplementary Materials available online.     

Inverse Problem for a Class of Two-Dimensional Diffusion Equations with Piecewise Constant Coefficients

In this paper, we consider an inverse problem for a class of two-dimensional diffusion equations with piecewise constant coefficients. This problem is studied using an explicit formula for the relevant spectral measures and an asymptotic expansion of the solution of the diffusion equations. A numerical method that reduces the inverse problem to a sequence of nonlinear least-square problems is proposed and tested on synthetic data.     

Adaptive Mollifiers for High Resolution Recovery of Piecewise Smooth Data from its Spectral Information

We discuss the reconstruction of piecewise smooth data from its (pseudo-) spectral information. Spectral projections enjoy superior resolution provided the data is globally smooth, while the presence of jump discontinuities is responsible for spurious O (1) Gibbs oscillations in the neighborhood of edges and an overall deterioration of the unacceptable first-order convergence in rate. The purpose is to regain the superior accuracy in the piecewise smooth case, and this is achieved by mollification. Here we utilize a modified version of the two-parameter family of spectral mollifiers introduced by Gottlieb and Tadmor [GoTa85]. The ubiquitous one-parameter, finite-order mollifiers are based on dilation . In contrast, our mollifiers achieve their high resolution by an intricate process of high-order cancellation . To this end, we first implement a localization step using an edge detection procedure [GeTa00a, b]. The accurate recovery of piecewise smooth data is then carried out in the direction of smoothness away from the edges, and adaptivity is responsible for the high resolution. The resulting adaptive mollifier greatly accelerates the convergence rate, recovering piecewise analytic data within exponential accuracy while removing the spurious oscillations that remained in [GoTa85]. Thus, these adaptive mollifiers offer a robust, general-purpose ``black box' procedure for accurate post-processing of piecewise smooth data. March 29, 2001. Final version received: August 31, 2001.     

一阶具有分段常数变量的微分方程的反周期和非线性边值问题

该文利用上下藕合解和单调迭代法，讨论了一阶具有分段常数变量微分方程的反边值和非线性边值问题x′(t)=f(t,x(t),x(［t-k］)), x(0)+h(x(T))=0, 这里h(θ)∈C\+1(R), h′(θ)>0，获得了这些问题的解的存在和唯一性.     

A Comparison of an Analytical Approach and a Standard Simulation Approach in Bayesian Forecasting Applied to Monthly Data from Insurance of Companies

In this paper, we apply the theory of Bayesian forecasting and dynamic linear models, as presented in West and Harrison (1997), to monthly data from insurance of companies. The total number reported claims of compensation is chosen to be the primary time series of interest. The model is decomposed into a trend block, a seasonal effects block and a regression block with a transformed number of policies as regressor. An essential part of the West and Harrison (1997) approach is to find optimal discount factors for each block and hence avoid the specification of the variance matrices of the error terms in the system equations. The BATS package of Pole et al. (1994) is applied in the analysis. We compare predictions based on this analytical approach with predictions based on a standard simulation approach applying the BUGS package of Spiegelhalter et al. (1995). The motivation for this comparison is to gain knowledge on the quality of predictions based on more or less standard simulation techniques in other applications where an analytical approach is impossible. The predicted values of the two approaches are very similar. The uncertainties, however, in the predictions based on the simulation approach are far larger especially two months or more ahead. This partly indicates the advantages of applying optimal discount factors and partly the disadvantages of at least a standard simulation approach for long term predictions.     

基于分段常值水平集的抛物型方程的参数识别算法

研究了线性抛物型方程不连续参数的识别算法.根据原有算法对于加噪观测数据计算不收敛的问题,本文基于分段常值水平集方法,根据水平集函数和优化过程的特点,修正原有Uzawa型算法中的带有总变差(TV)正则化的极小化模型和对常值向量的极小化模型,并且利用分裂Bregman迭代算法处理TV范数的优越性,构造一种新的参数识别算法格式.数值实验结果显示,新算法具有计算时间短、精度高、抗噪性强的优点.     

General Algebraic Structures of Clifford Type and Cauchy-Pompeiu Formulae for Some Piecewise Constant Structure Relations

The paper starts with generalization of Clifford algebras using other structure relations which possibly depend on spacelike variables. For piecewise constant structure relations the paper constructs fundamental solutions explicitly and proves a Cauchy-Pompeiu Integral Formula.     

A generalization of the Discrete Isoperimetric Inequality for Piecewise Smooth Curves of Constant Geodesic Curvature

Summary The discrete isoperimetric problem is to determine the maximal area polygon with at most <InlineEquation ID=IE"1"><EquationSource Format="TEX"><![CDATA[<InlineEquation ID=IE"2"><EquationSource Format="TEX"><![CDATA[$]]></EquationSource></InlineEquation>]]></EquationSource></InlineEquation>k$ vertices and of a given perimeter. It is a classical fact that the unique optimal polygon on the Euclidean plane is the regular one. The same statement for the hyperbolic plane was proved by K\'aroly Bezdek [1] and on the sphere by L\'aszl\'o Fejes T\'oth [3]. In the present paper we extend the discrete isoperimetric inequality for ``polygons' on the three planes of constant curvature bounded by arcs of a given constant geodesic curvature.     

A Direct Method for Solving the Inverse Coefficient Problem for an Elliptic Equation with Piecewise Constant Coefficients

Journal of Applied and Industrial Mathematics - Some direct numerical method is presented for solving the inverse coefficient problem for an elliptic equation with piecewise constant coefficients....     

Two-Stage Bayesian Approach for GWAS With Known Genealogy

Genome-wide association studies (GWAS) aim to assess relationships between single nucleotide polymorphisms (SNPs) and diseases. They are one of the most popular problems in genetics, and have some peculiarities given the large number of SNPs compared to the number of subjects in the study. Individuals might not be independent, especially in animal breeding studies or genetic diseases in isolated populations with highly inbred individuals. We propose a family-based GWAS model in a two-stage approach comprising a dimension reduction and a subsequent model selection. The first stage, in which the genetic relatedness between the subjects is taken into account, selects the promising SNPs. The second stage uses Bayes factors for comparison among all candidate models and a random search strategy for exploring the space of all the regression models in a fully Bayesian approach. A simulation study shows that our approach is superior to Bayesian lasso for model selection in this setting. We also illustrate its performance in a study on Beta-thalassemia disorder in an isolated population from Sardinia. Supplementary Material describing the implementation of the method proposed in this article is available online.     

Nonparametric Bayesian Modeling for Multivariate Ordinal Data

This article proposes a probability model for k-dimensional ordinal outcomes, that is, it considers inference for data recorded in k-dimensional contingency tables with ordinal factors. The proposed approach is based on full posterior inference, assuming a flexible underlying prior probability model for the contingency table cell probabilities. We use a variation of the traditional multivariate probit model, with latent scores that determine the observed data. In our model, a mixture of normals prior replaces the usual single multivariate normal model for the latent variables. By augmenting the prior model to a mixture of normals we generalize inference in two important ways. First, we allow for varying local dependence structure across the contingency table. Second, inference in ordinal multivariate probit models is plagued by problems related to the choice and resampling of cutoffs defined for these latent variables. We show how the proposed mixture model approach entirely removes these problems. We illustrate the methodology with two examples, one simulated dataset and one dataset of interrater agreement.     

Variational Approach for Learning Markov Processes from Time Series Data

Journal of Nonlinear Science - Inference, prediction, and control of complex dynamical systems from time series is important in many areas, including financial markets, power grid management,...     

一类具有逐段常变量神经网络系统概周期解存在性与指数稳定性

利用Halanay不等式,我们得到了一类具有逐段常变量神经网络系统解全局指数稳定的一个充分条件;此外,在同样条件下,通过不动点定理,证明了这类概周期系统概周期解的存在唯一性与模包含关系．