A New Unified Approach for the Simulation of a Wide Class of Directional Distributions

The need for effective simulation methods for directional distributions has grown as they have become components in more sophisticated statistical models. A new acceptance–rejection method is proposed and investigated for the Bingham distribution on the sphere using the angular central Gaussian distribution as an envelope. It is shown that the proposed method has high efficiency and is also straightforward to use. Next, the simulation method is extended to the Fisher and Fisher–Bingham distributions on spheres and related manifolds. Together, these results provide a widely applicable and efficient methodology to simulate many of the standard models in directional data analysis. An R package simdd, available in the online supplementary material, implements these simulation methods.     

水滴清除气溶胶过程的随机算法和数值模拟

气溶胶尺度分布的时间演变可量化气溶胶的湿沉降过程,它在数学上可由考虑湿沉降的通用动力学方程来描述．该方程为一典型的部分积分微分方程,与气溶胶尺度分布和雨滴尺度分布均相关,且由于需要考虑Brown扩散、拦截和惯性碰撞等湿沉降机制而使得清除系数模型非常复杂,普通的数值方法难以求解．为此发展了一种新的多重Monte Carlo算法,以求解考虑湿沉降的通用动力学方程,并用于模拟实际环境中气溶胶的湿沉降．对于对数正态分布的气溶胶尺度分布和雨滴尺度分布, 多重Monte Carlo算法进行的数值模拟表明, 雨滴几何平均尺度越小, 雨滴几何标准偏差越小,越有利于小尺度和中等尺度气溶胶的湿去除,而稍微不利于大尺度气溶胶的湿去除．     

正态分布和瑞利分布混合情形下的参数估计及分类问题

学者往往用单一的分布模拟和拟合杂波,如正态分布、瑞利分布和威布尔分布等。然而在实际中,雷达杂波由多种类型的杂波组成,单一分布通常不能精确刻画雷达杂波规律,因此,应用混合分布模型对雷达杂波数据建模更准确。本文考虑用正态分布和瑞利分布的混合分布拟合杂波,并应用矩估计方法和基于EM算法的极大似然估计方法估计模型参数,最后,应用最大后验概率分类准则验证2种估计方法的分类准确率。通过数据模拟,得出极大似然估计的效果和分类准确率都要优于矩估计的估计效果和分类准确率。     

几种模拟逐日降水的分布函数比较分析

在模拟逐日降水方面,将马尔科夫链和某种分布函数相结合建立随机模型的方法在国外很多地区被证明是有效的,但该方法在我国的适用性研究并不多见.本文应用哈尔滨48年的实测降水资料,基于一阶马尔科夫两状态转移概率,分别采用皮尔逊Ⅲ型分布、伽玛分布和偏正态分布函数模拟了哈尔滨的1000年逐日降水过程.对比分析结果表明,伽玛分布更适合模拟哈尔滨的逐日降水过程,基于伽玛分布函数模拟的月份降水量和降水天数与实测降水数据符合较好.     

Stochastic Modeling of Congress

We analyze the dynamics of growth of the number of congressmen supporting the resolution HR1207 to audit the Federal Reserve. The plot of the total number of cosponsors as a function of time is of the “Devil's staircase” type. The distribution of the numbers of new cosponsors joining during a particular day (step height) follows a power law. The distribution of the length of intervals between additions of new cosponsors (step length) also follows a power law. We use a modification of Bak-Tang-Wiesenfeld sandpile model to simulate the dynamics of Congress and obtain good agreement with the data.     

Modeling of non-stationary ground motion using the mean reverting stochastic process

This paper presents a new method for modeling amplitude and frequency non-stationary earthquake ground motions using a scalar first order dynamic mean reverting stochastic differential equation driven by Brownian motion with parametric time varying coefficients. It determines the proper relationship between these time varying parametric coefficients and presents the statistical and probability distribution characteristics of the response solution. It demonstrates the applicability of the method by presenting some simulations of amplitude and frequency non-stationary earthquake ground motions. The verification of the amplitude and frequency non-stationary contents of the mean reverting stochastic ground motions is demonstrated using the Hilbert–Huang transform method. Also a corresponding interpretation between the coefficients of the proposed model and the coefficients of the usual oscillatory second order differential equation driven by white Gaussian noise is presented along with some comments how it can be applied to simulate ground motions consistent with acceleration target records such as boxcar, trapezoidal, other exponential functions, or compound and target records at source, near field, and far field distances.     

专利申请与授权量的时间序列分析

专利的申请和授权量动态分析与预测是建立专利预警机制、设计与制定相关政策和战略的基础,具有重要的理论意义。起伏型时间序列法是一种新的时间序列分析法,提出用起伏型时间序列法对专利申请与授权数据进行动态分析。以2005年国内专利申请与授权数量月动态为研究数据,对专利申请、授权及发明专利申请数量的月动态进行建模模拟,结果令人满意,说明起伏型时间序列分析方法可应用于专利申请与授权动态模拟,从而丰富了专利申请与授权数据动态分析方法。     

Stability of multiquadric quasi-interpolation to approximate high order derivatives

Numerical simulation of the high order derivatives based on the sampling data is an important and basic problem in numerical approximation,especially for solving the differential equations numerically.The classical method is the divided difference method.However,it has been shown strongly unstable in practice.Actually,it can only be used to simulate the lower order derivatives in applications.To simulate the high order derivatives,this paper suggests a new method using multiquadric quasi-interpolation.The s...     

Oil production: A probabilistic model of the Hubbert curve

The specific distribution of hydrocarbon field sizes has a deep impact on the dynamics of the production inside a basin. This paper proposes a probabilistic model based on the field size distribution, taking into account the launching production process of the fields. This model can be seen as a ‘bottom‐up’ probabilistic method to simulate and predict oil production. The asymptotic results obtained allow us to propose relevant fitting to real production curves. Copyright © 2010 John Wiley & Sons, Ltd.     

Laplace Expansions in Markov chain Monte Carlo Algorithms

Complex hierarchical models lead to a complicated likelihood and then, in a Bayesian analysis, to complicated posterior distributions. To obtain Bayes estimates such as the posterior mean or Bayesian confidence regions, it is therefore necessary to simulate the posterior distribution using a method such as an MCMC algorithm. These algorithms often get slower as the number of observations increases, especially when the latent variables are considered. To improve the convergence of the algorithm, we propose to decrease the number of parameters to simulate at each iteration by using a Laplace approximation on the nuisance parameters. We provide a theoretical study of the impact that such an approximation has on the target posterior distribution. We prove that the distance between the true target distribution and the approximation becomes of order O(N^?a) with a ∈ (0, 1), a close to 1, as the number of observations N increases. A simulation study illustrates the theoretical results. The approximated MCMC algorithm behaves extremely well on an example which is driven by a study on HIV patients.     

Numerical simulations and modeling for stochastic biological systems with jumps

This paper gives a numerical method to simulate sample paths for stochastic differential equations (SDEs) driven by Poisson random measures. It provides us a new approach to simulate systems with jumps from a different angle. The driving Poisson random measures are assumed to be generated by stationary Poisson point processes instead of Lévy processes. Methods provided in this paper can be used to simulate SDEs with Lévy noise approximately. The simulation is divided into two parts: the part of jumping integration is based on definition without approximation while the continuous part is based on some classical approaches. Biological explanations for stochastic integrations with jumps are motivated by several numerical simulations. How to model biological systems with jumps is showed in this paper. Moreover, method of choosing integrands and stationary Poisson point processes in jumping integrations for biological models are obtained. In addition, results are illustrated through some examples and numerical simulations. For some examples, earthquake is chose as a jumping source which causes jumps on the size of biological population.     

On the design of an upwind scheme for compressible flow on general triangulations

We discuss the design features and mathematical background of an explicit upwind finite-volume method to simulate non-stationary flow of a compressible, inviscid fluid. One of the design goals was the rigorous mathematical justification of each ingredient of the method. The method itself contains elements from finite-difference methods as well as finite-element methods and is formulated in a finite volume framework. The use of well-known algorithmic ingredients in a new framework results in a robust time-accurate scheme. To be able to easily handle complex geometries as well as adaption algorithms a tringale-based formulation was chosen. Numerical tests for two-dimensional flow are presented.     

Problem with nonequilibrium boundary conditions in the kinetic theory of gases

The Boltzmann kinetic equation is considered in a new formulation with nonequilibrium distribution functions on free boundaries, which makes it possible to simulate nonequilibrium superand subsonic flows. Transport processes for such flows are analyzed. The possibility of anomalous transport is determined, in which case the heat flux, temperature gradient, and the corresponding components of the nonequilibrium stress tensor and the velocity gradient have the same sign.     

基于MCMC模拟方法的带变点的NHPP软件可靠性模型

本文主要讨论软件测试过程中NHPP模型参数发生变化的情形,并用Bayes方法对GGO模型进行变点分析,运用基于Gibbs抽样的MCMC方法模拟出参数后验分布的马尔科夫链,最后借助于BUGS软件包对软件故障数据集Musa进行建模仿真,其结果表明该模型在软件可靠性变点分析中的直观性和有效性。     

一维高精度离散GDQ方法

GDQ method is a kind of high order accurate numerical methods developed several years ago, which have been successfully used to simulate the solution of smooth engineering problems such as structure mechanics and incompressible fluid dynamics. In this paper, extending the traditional GDQ method, we develop a new kind of discontinuous GDQ methods to solve compressible flow problems of which solutions may be discontinuous. In order to capture the local features of fluid flows, firstly, the computational domain is divided into many small pieces of subdomains. Then, in each small subdomain, the GDQ method is implementedand some kinds of numerical flux limitation conditions will be required to keep the correct flow direction. At the boundary interface between subdomains, we also use some kind of flux conditions according to the flow direction. The numerical method obtained by the above steps has the advantages of high order accuracy and easy to treat boundary conditions. It can simulate perfectly nonlinear waves such as shock, rarefaction wave and contact discontinuity. Finally, the numerical experiments on one dimensional Burgers equation and Euler equations are given.The numerical results verify the validation of the method.     

关于《保辛水波动力学》的一个注记

研究完全非线性水波的数值模拟方法，将《保辛水波动力学》中提出的保辛摄动方法，扩展到非线性水波压强的分析.数值算例表明，该文方法可用于水波的非线性演化分析，也可用于模拟孤立波、尖锐波峰的涌波等非线性水波，并给出水波的压强分布.     

An accept-reject algorithm for the positive multivariate normal distribution

The need to simulate from a positive multivariate normal distribution arises in several settings, specifically in Bayesian analysis. A variety of algorithms can be used to sample from this distribution, but most of these algorithms involve Gibbs sampling. Since the sample is generated from a Markov chain, the user has to account for the fact that sequential draws in the sample depend on one another and that the sample generated only follows a positive multivariate normal distribution asymptotically. The user would not have to account for such issues if the sample generated was i.i.d. In this paper, an accept-reject algorithm is introduced in which variates from a positive multivariate normal distribution are proposed from a multivariate skew-normal distribution. This new algorithm generates an i.i.d. sample and is shown, under certain conditions, to be very efficient.     

Numerical investigation of blood flow. Part I: In microvessel bifurcations

In some diseases there is a focal pattern of velocity in regions of bifurcation, and thus the dynamics of bifurcation has been investigated in this work. A computational model of blood flow through branching geometries has been used to investigate the influence of bifurcation on blood flow distribution. The flow analysis applies the time-dependent, three-dimensional, incompressible Navier–Stokes equations for Newtonian fluids. The governing equations of mass and momentum conservation were solved to calculate the pressure and velocity fields. Movement of blood flow from an arteriole to a venule via a capillary has been simulated using the volume of fluid (VOF) method. The proposed simulation method would be a useful tool in understanding the hydrodynamics of blood flow where the interaction between the RBC deformation and blood flow movement is important. Discrete particle simulation has been used to simulate the blood flow in a bifurcation with solid and fluid particles. The fluid particle method allows for modeling the plasma as a particle ensemble, where each particle represents a collective unit of fluid, which is defined by its mass, moment of inertia, and translational and angular momenta. These kinds of simulations open a new way for modeling the dynamics of complex, viscoelastic fluids at the micro-scale, where both liquid and solid phases are treated with discrete particles.     

Self-adaptive FEM numerical modeling of the mild-slope equation

Based on the linear wave theory, the mild-slope equation (MSE) is a preferred mathematical model to simulate nearshore wave propagation. A numerical model to solve the MSE is developed here on the basis of a self-adaptive finite element model (FEM) combined with an iterative method to determine the wave direction angle to the boundary and thus to improve the treatment of the boundary conditions. The numerical resolution of the waves into ideal domains and multidirectional waves through a breakwater gap shows that the numerical model developed here is effective in representing wave absorption at the absorbing boundaries and can be used to simulate multidirectional wave propagation. Finally, the simulated wave distribution in a real harbor shows that the numerical model can be used for engineering practice.     

Bootstrapping Log Likelihood and EIC, an Extension of AIC

Akaike (1973, 2nd International Symposium on Information Theory, 267-281,Akademiai Kiado, Budapest) proposed AIC as an estimate of the expected loglikelihood to evaluate the goodness of models fitted to a given set of data.The introduction of AIC has greatly widened the range of application ofstatistical methods. However, its limit lies in the point that it can beapplied only to the cases where the parameter estimation are performed bythe maximum likelihood method. The derivation of AIC is based on theassessment of the effect of data fluctuation through the asymptoticnormality of MLE. In this paper we propose a new information criterion EICwhich is constructed by employing the bootstrap method to simulate the datafluctuation. The new information criterion, EIC, is regarded as an extensionof AIC. The performance of EIC is demonstrated by some numerical examples.