Estimating Orthant Probabilities of High-Dimensional Gaussian Vectors with An Application to Set Estimation

The computation of Gaussian orthant probabilities has been extensively studied for low-dimensional vectors. Here, we focus on the high-dimensional case and we present a two-step procedure relying on both deterministic and stochastic techniques. The proposed estimator relies indeed on splitting the probability into a low-dimensional term and a remainder. While the low-dimensional probability can be estimated by fast and accurate quadrature, the remainder requires Monte Carlo sampling. We further refine the estimation by using a novel asymmetric nested Monte Carlo (anMC) algorithm for the remainder and we highlight cases where this approximation brings substantial efficiency gains. The proposed methods are compared against state-of-the-art techniques in a numerical study, which also calls attention to the advantages and drawbacks of the procedure. Finally, the proposed method is applied to derive conservative estimates of excursion sets of expensive to evaluate deterministic functions under a Gaussian random field prior, without requiring a Markov assumption. Supplementary material for this article is available online.     

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??An absorbing Markov chain is an important statistic model and widely used in algorithm modeling for many disciplines, such as digital image processing, network analysis and so on. In order to get the stationary distribution for such model, the inverse of the transition matrix usually needs to be calculated. However, it is still difficult and costly for large matrices. In this paper, for absorbing Markov chains with two absorbing states, we propose a simple method to compute the stationary distribution for models with diagonalizable transition matrices. With this approach, only an eigenvector with eigenvalue 1 needs to be calculated. We also use this method to derive probabilities of the gambler's ruin problem from a matrix perspective. And, it is able to handle expansions of this problem. In fact, this approach is a variant of the general method for absorbing Markov chains. Similar techniques can be used to avoid calculating the inverse matrix in the general method.     

Optimal non-proportional reinsurance control

This paper deals with the problem of ruin probability minimization under various investment control and reinsurance schemes. We first look at the minimization of ruin probabilities in the models in which the surplus process is a continuous diffusion process in which we employ stochastic control to find the optimal policies for reinsurance and investment. We then focus on the case in which the surplus process is modeled via a classical Lundberg process, i.e. the claims process is compound Poisson. There, the optimal reinsurance policy is derived from the Hamilton-Jacobi-Bellman equation.     

MAXIMAL ATTRACTORS FOR THE COMPRESSIBLE NAVIER-STOKES EQUATIONS OF VISCOUS AND HEAT CONDUCTIVE FLUID

This article is concerned with the existence of maximal attractors in Hi(i=1,2,4) for the compressible Navier-Stokes equations for a polytropic viscous heat conduc-tive ideal gas in bounded annular domains Ωn in Rn(n =2,3).One of the important features is that the metric spaces H(1),H(2),and H(4) we work with are three incomplete metric spaces,as can be seen from the constraints θ0 and u0,with θ and u being absolute temperature and specific volume respectively.For any constants δ1,δ2,···,δ8 verifying some conditions,a sequence of closed subspaces Hδ(i)H(i)(i = 1,2,4) is found,and the existence of maximal(universal) attractors in Hδ(i)(i = 1,2,4) is established.     

An ensemble method using credal decision trees

Supervised classification learning can be considered as an important tool for decision support. In this paper, we present a method for supervised classification learning, which ensembles decision trees obtained via convex sets of probability distributions (also called credal sets) and uncertainty measures. Our method forces the use of different decision trees and it has mainly the following characteristics: it obtains a good percentage of correct classifications and an improvement in time of processing compared with known classification methods; it not needs to fix the number of decision trees to be used; and it can be parallelized to apply it on very large data sets.     

Global attractor for a nonlinear plate equation with supported boundary conditions

In this paper, we consider a two-dimensional nonlinear equation

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高斯光束中吸收双层球形微粒的横向光俘获

为了研究吸收双层球形微粒的横向光俘获,基于几何光学模型提出了双层带吸收球形微粒的光俘获模型,对TEM₀₀模式高斯光束照射下外层有光吸收的双层电介质球形微粒受到的横向光俘获力进行了数值模拟,取得了光俘获力特性的一系列结果.结果显示,双层球形微粒的外层吸收系数对包括稳态俘获位置,峰值强度,稳态俘获的刚度等光俘获特性有很大影响.此外,内外径的比率对吸收双层球形微粒的光俘获特性也有调制性的影响.在一定条件下,带吸收的双层球形微粒可以被俘获在光轴上,也可能被俘获在中心在光轴上的圆环上. 关键词： 光俘获 几何光学模型 高斯光束 吸收双层球     

介质层厚度无序对Bragg微腔模式的影响

采用本征模展开法(EME)结合完全匹配层（PML）边界条件,研究了由TiO2和SiO2复合膜结构组成的平面光子晶体Bragg微腔的模式特性,分析了介质厚度无序对微腔模式的调制以及入射角对局域长度的影响.结果表明,若光束正入射,带边局域长度要大于禁带局域长度,随着无序度的增加光子通带的透过率逐渐降低,而禁带的透过率逐渐上升.当无序度较小时,局域长度随随机度的变化在带边和禁带内表现出相反的规律.当无序度较大时,局域长度不仅和随机度、带隙有关,还受到材料的影响;若光束斜入射,TE模的局域长度要远小于TM模对应的值,且其最小值向短波方向移动。此外,入射角和膜层数的变化都会导致局域长度的起伏.     

Effect of spontaneously generated coherence on EIT and its refractive properties in four- and five-levels systems

The effects of spontaneously generated coherence (SGC) and phases of optical fields on the phenomenon of electromagnetically induced transparency (EIT) are investigated in a four-level inverted-Y system and in a five-level K-type system under various parametric conditions in order to demonstrate controllability of the EIT, dispersion properties, and group velocity in such systems. Non-zero second-order susceptibility in both systems is due to the SGC effect. The experimental viability of the model in semiconductor quantum well systems is also discussed.