The multifractal spectrum of statistically self-similar measures

We calculate the multifractal spectrum of a random measure constructed using a statistically self-similar process. We show that with probability one there is a multifractal decomposition analogous to that in the deterministic self-similar case, with the exponents given by the solution of an expectation equation.     

Global optimization by random perturbation of the gradient method with a fixed parameter

The paper deals with the global minimization of a differentiable cost function mapping a ball of a finite dimensional Euclidean space into an interval of real numbers. It is established that a suitable random perturbation of the gradient method with a fixed parameter generates a bounded minimizing sequence and leads to a global minimum: the perturbation avoids convergence to local minima. The stated results suggest an algorithm for the numerical approximation of global minima: experiments are performed for the problem of fitting a sum of exponentials to discrete data and to a nonlinear system involving about 5000 variables. The effect of the random perturbation is examined by comparison with the purely deterministic gradient method.     

Uniform random number generation

In typical stochastic simulations, randomness is produced by generating a sequence of independent uniform variates (usually real-valued between 0 and 1, or integer-valued in some interval) and transforming them in an appropriate way. In this paper, we examine practical ways of generating (deterministic approximations to) such uniform variates on a computer. We compare them in terms of ease of implementation, efficiency, theoretical support, and statistical robustness. We look in particular at several classes of generators, such as linear congruential, multiple recursive, digital multistep, Tausworthe, lagged-Fibonacci, generalized feedback shift register, matrix, linear congruential over fields of formal series, and combined generators, and show how all of them can be analyzed in terms of their lattice structure. We also mention other classes of generators, like non-linear generators, discuss other kinds of theoretical and empirical statistical tests, and give a bibliographic survey of recent papers on the subject.     

Rate conservation laws: A survey

We survey the rate conservation law, RCL for short, arising in queues and related stochastic models. RCL was recognized as one of the fundamental principles to get relationships between time and embedded averages such as the extended Little's formulaH=G, but we show that it has other applications. For example, RCL is one of the important techniques for deriving equilibrium equations for stochastic processes. It is shown that the various techniques, including Mecke's formula for a stationary random measure, can be formulated as RCL. For this purpose, we start with a new definition of the rate with respect to a random measure, and generalize RCL by using it. We further introduce the notion of quasi-expectation, which is a certain extension of the ordinary expectation, and derive RCL applicable to the sample average results. It means that the sample average formulas such asH=G can be obtained as the stationary RCL in the quasi-expectation framework. We also survey several extensions of RCL and discuss examples. Throughout the paper, we would like to emphasize how results can be easily obtained by using a simple principle, RCL.     

Random sampling within the framework of a multivariate principal-agent approach

In this paper, we analyze a specific class of principal-agent models which seems to be sufficiently general to cover applications in environmental economics with upstream-downstream problems as an example. In our basic model, the observation outcome is ann-dimensional random vectorx and only the first and second moments ofx are common knowledge. We study the effects of random sampling in the presence of costly signals. For this purpose, we assume that the principal and the agent use a simple statistical procedure, i.e. their contract will be based on the mean of a random sample with sampling costs dependent on the sample size. It is shown that there exists an optimal sample size. We investigate the relationship between the optimal sample size, the marginal sampling costs, and the agent's risk aversion.     

Asymptotic distribution of maximal autoregressive process with weight tending to 1

The maximal operator plays the similar role as the summation operator in the sense of stability of operation. So, we could discussARMA processes in the maximal operation by the same way as in the summation operation. However, many papers already treated with moving order statistics. In this paper, we discuss asymptotic behaviors of maximal autoregressive (MAR) processes with the weight tending to 1.     

随机扰动下三维流体界面不稳定性的并行计算

对三维流体界面不稳定性的数值模拟引进了新的数值计算方法,并在MPI并行计算环境下进行了数值模拟.利用LevelSet方法确定界面位置,零水平集对应界面位置.对应离散LevelSet方程和界面两侧的两套Euler方程,借助于Ghost网格方法来完成离散.对最后网格点上的两套状态量的辨认依赖于该点的LevelSet值的符号.并进行了数值计算.     

基于小波-双马尔可夫随机场的纹理分割

传统高斯金字塔马尔可夫随机场模型仅仅捕捉图像低频信息,纹理分割效果不太理想.根据纹理图像小波分解后各频带的统计性质和层次关系,优化频带选取,提出了一种变形小波结构,建立了融合这种结构上尺度内部和尺度之间关系的双马尔可夫随机场模型,引入了一种近似最大联合概率分割算法,并从理论上分析了该算法的合理性.实验表明,与基于高斯金字塔马尔可夫随机场模型的分割方法相比,该算法分割质量明显提高;并且,对模型中自由参数的选取进行比较,证实它们在给定区间上的选择具有鲁棒性.     

行NA随机变量三角阵列的完全收敛性

令{Xm;1≤i≤n,n≥1}是行NA的随机变量三角阵列．利用NA随机变量序列的一个矩不等式,讨论了行NA的随机变量三角阵列在被随机变量X弱平均控制的条件下的完全收敛性．所得到的结果推广了行独立的随机变量三角阵列相应的结果．     

随机环境马氏链上一类集合的分形维数

假设{Xn}是随机环境的马氏链，引入Hopf Markov链，在此基础上定义了绕积Markov链，用此方法，将正则马氏链下一类集合的分形维数的结果推广到了随机环境马氏链下一类集合，并且获得了相应的结论．