用类同余法产生随机数及其检验

模拟随机过程的各种模型都需要用到大量随机数 ,而各种分布的随机样本又可以由U(0 ,1)来产生 ,所以如何产生性能好、成本低、使用方便的随机数具有重要意义。本文介绍了一种随机数的产生方法并对其进行了严格的检验。     

复杂网络统计力学的差分方程方法

通过分析几种估计增长网络度分布方法的缺点,提出估计度分布的差分方程方法,不仅避免了复杂网络分析中将离散问题连续化带来的逻辑矛盾,也避免了网络稳态度分布存在性的假设.利用这个方法给出Poisson增长择优连接网络的度分布公式,借助Poisson过程理论和Gamma 分布的性质严格证明Poisson增长择优连接网络是无标度网络.     

非线性Poisson-Gamma回归模型中存在偏大离差时离差参数的齐性检验

Poisson回归模型广泛地应用于分析计数型数据，但该模型往往存在偏大离差(overdispersion)问题．刻画Poisson回归模型的偏大离差性的两种方法是拟似然方法和随机效应法(Lee＆Nelder，2000)，已有许多作者利用随机效应法研究了Poisson模型的偏大离差的检验问题．但他们均假定随机效应是独立同分布的，本文对他们的假设进行检验．我们分别在组内效应一致和组内效应不一致的情形下，研究了存在偏大离差的Poisson-Gamma非线性随机效应模型中，随机效应方差(称为离差参数)的齐性检验问题，得到了离差参数齐性的score检验统计量．最后给出两个数值例子说明本文方法的应用．     

计算机产生随机数的方法

2003年中华人民共和国教育部颁布的《普通高中数学课程标准(实验稿)》在必修3中增加了“了解随机数的意义,能运用模拟方法估计概率”的内容,那么什么是随机数?计算机中的随机数是如何产生的?1随机数与伪随机数设随机变量η的分布函数为F(x),则称随机变量η的随机抽样序列{ηi}为分布函数F(x)的随机数.事实上,随机数{ηi}就是随机变量η的观测值,或者说是来自随机变量η的样本.随机数一定是相对某一个确定分布而言的.若随机变量η服从正态分布N(μ,σ2),则称来自η的随机抽样序列{ηi}为正态分布随机数;若随机变量η服从[0,1]区间上均匀分…     

马尔可夫调制及带Poisson跳随机时滞微分方程依分布稳定

本文讨论马尔可夫调制及带Poisson跳随机时滞微分方程,其主要目的是研究方程解的依分布稳定.     

带Poisson 跳跃的正倒向随机延迟系统递归最优控制问题的最大值原理

本文研究了带Poisson 跳跃的正倒向随机延迟系统的递归最优控制问题. 利用经典的针状变分方法、对偶技术和带Poisson 跳跃的超前倒向随机微分方程的相关结果, 证明了最优控制的最大值原理, 包括了最优控制满足的必要条件和充分条件.     

关于二项分布、Poisson分布和几何分布的高阶矩的递推公式

给出了二项分布、Poisson分布和几何分布高阶矩的递推公式,避免了其它计算方法上的不便与误差.     

舍选法的几何解释及其应用

本文给出了统计模拟中随机数生成之舍选法的几何解释 ,并将其应用到三角形分布和指数型分布的随机数生成算法中     

Poisson跳的随机延迟微分方程Heun方法的均方收敛性

Heun方法是一类求解随机延迟微分方程的数值方法,本文试图研究Poisson跳的随机延迟微分方程Heun方法的均方收敛性.当Poisson跳的随机延迟微分方程满足一定约束条件时,获得Heun方法求解方程所得的数值解收敛于真解,且均方收敛阶为1的理论结果2.文末数值试验的结果验证了理论结果的正确性.     

蒙特卡洛方法(Ⅱ)

上面几节讨论了随机数、随机变量、随机过程的模拟方法.很自然地要问,用这些方法产生的数值序列,具有我们所要求的统计性质吗?能够在蒙特卡洛模拟过程中使用吗?这里,我们从统计假设检验出发,分析它们的统计性质,讨论并解决上述问题.设随机变量η具有连续的分布函数 F(x),则随机变量     

赌博破产概率及其随机模拟试验

讨论了赌博问题中的最终破产概率,并给出了破产概率的随机模拟计算流程和一个具体例子的数值模拟结果.计算结果表明,由此方法得到破产概率的估计值与理论值的误差很小.最后,通过随机模拟给出游戏结束的平均次数.     

Simulation of stochastic integrals with respect to Lévy processes of type G

We study the simulation of stochastic processes defined as stochastic integrals with respect to type G Lévy processes for the case where it is not possible to simulate the type G process exactly. The type G Lévy process as well as the stochastic integral can on compact intervals be represented as an infinite series. In a practical simulation we must truncate this representation. We examine the approximation of the remaining terms with a simpler process to get an approximation of the stochastic integral. We also show that a stochastic time change representation can be used to obtain an approximation of stochastic integrals with respect to type G Lévy processes provided that the integrator and the integrand are independent.     

Effective averaging of stochastic radiative models based on Monte Carlo simulation

Based on the Monte Carlo simulation and probabilistic analysis, stochastic radiative models are effectively averaged; that is, deterministic models that reproduce the mean probabilities of particle passage through a stochastic medium are constructed. For this purpose, special algorithms for the double randomization and conjugate walk methods are developed. For the numerical simulation of stochastic media, homogeneous isotropic Voronoi and Poisson mosaic models are used. The parameters of the averaged models are estimated based on the properties of the exponential distribution and the renewal theory.     

A stochastic approach for simulating spatially inhomogeneous coagulation dynamics in the gelation regime

We present a stochastic approach for the simulation of coagulation–diffusion dynamics in the gelation regime. The method couples the mass flow algorithm for coagulation processes with a stochastic variant of the diffusion-velocity method in a discretized framework. The simulation of the stochastic processes occurs according to an optimized implementation of the principle of grouping the possible events. A full simulation of a particle system driven by coagulation–diffusion dynamics is performed with a high degree of accuracy. This allows a qualitative and quantitative analysis of the behaviour of the system. The performance of the method becomes more evident especially in the gelation regime, where the computations become usually very time consuming.     

MC方差减小技术在算术平均亚式外汇期权定价中的应用

建立了利率和汇率波动率均为随机情形下算术平均亚式外汇期权的定价模型.由于其定价问题求解十分困难,运用蒙特卡罗(Monte Carlo)方法并结合控制变量方差减小技术进行模拟,有效地减小了模拟方差,得到了期权定价问题的数值结果.     

Stochastic asymptotical stability for stochastic impulsive differential equations and it is application to chaos synchronization

In this paper, the stochastic asymptotical stability of stochastic impulsive differential equations is studied, and a comparison theory about the stochastic asymptotical stability of trivial solution is established. From the comparison theory, we can find out whether the stochastic impulsive differential system is stochastic asymptotically stable by studying the stability of a deterministic comparison system. As an application of this theory, we study the problem of chaos synchronization in Chua circuit using impulsive method. Finally, numerical simulation is employed to verify the feasibility of our method.     

Runge–Kutta Methods for Itô Stochastic Differential Equations with Scalar Noise

A general class of stochastic Runge–Kutta methods for Itô stochastic differential equation systems w.r.t. a one-dimensional Wiener process is introduced. The colored rooted tree analysis is applied to derive conditions for the coefficients of the stochastic Runge–Kutta method assuring convergence in the weak sense with a prescribed order. Some coefficients for new stochastic Runge–Kutta schemes of order two are calculated explicitly and a simulation study reveals their good performance.     

Multilevel hybrid split-step implicit tau-leap

In biochemically reactive systems with small copy numbers of one or more reactant molecules, the dynamics is dominated by stochastic effects. To approximate those systems, discrete state-space and stochastic simulation approaches have been shown to be more relevant than continuous state-space and deterministic ones. In systems characterized by having simultaneously fast and slow timescales, existing discrete space-state stochastic path simulation methods, such as the stochastic simulation algorithm (SSA) and the explicit tau-leap (explicit-TL) method, can be very slow. Implicit approximations have been developed to improve numerical stability and provide efficient simulation algorithms for those systems. Here, we propose an efficient Multilevel Monte Carlo (MLMC) method in the spirit of the work by Anderson and Higham (SIAM Multiscal Model. Simul. 10(1), 2012). This method uses split-step implicit tau-leap (SSI-TL) at levels where the explicit-TL method is not applicable due to numerical stability issues. We present numerical examples that illustrate the performance of the proposed method.     

Complex dynamics in a stochastic internal HIV model

In this paper, we analyze a stochastic model representing HIV internal virus dynamics. The stochasticity in the model is introduced by parameter perturbation which is a standard technique in stochastic population modeling. By analyzing the Lyapunov exponent, singular boundary and probability density, some new criteria ensuring stochastic stability, D-bifurcation and P-bifurcation for stochastic internal HIV model are obtained, respectively. Numerical simulation results are given to support the theoretical predictions.