Corrections to the Article “Weighted Monte Carlo Methods for Approximate Solution of a Nonlinear Boltzmann Equation”

Corrections are given to the above-mentioned article.     

Weighted Monte Carlo method for an approximate solution of the nonlinear coagulation equation

New weighted modifications of direct statistical simulation methods designed for the approximate solution of the nonlinear Smoluchowski equation are developed on the basis of stratification of the interaction distribution in a multiparticle system according to the index of a pair of interacting particles. The weighted algorithms are validated for a model problem with a known solution. It is shown that they effectively estimate variations in the functionals with varying parameters, in particular, with the initial number N ₀ of particles in the simulating ensemble. The computations performed for the problem with a known solution confirm the semiheuristic hypothesis that the model error is O(N ₀ ^?1 ). Estimates are derived for the derivatives of the approximate solution with respect to the coagulation coefficient.     

Optimization of Weighted Monte Carlo Methods with Respect to Auxiliary Variables

We consider the problems whose mathematical model is determined by some Markov chain terminating with probability one; moreover, we have to estimate linear functionals of a solution to an integral equation of the second kind with the corresponding substochastic kernel and free term [1]. To construct weighted modifications of numerical statistical models, we supplement the coordinates of the phase space with auxiliary variables whose random values functionally define the transitions in the initial chain. Having implemented each auxiliary random variable, we multiply the weight by the ratio of the corresponding densities of the initial and numerically modeled distributions. We solve the minimization problem for the variances of estimators of linear functionals by choosing the modeled distribution of the first auxiliary random variable.     

Sequential Monte Carlo Methods for Option Pricing

In this article, we provide a review and development of sequential Monte Carlo (SMC) methods for option pricing. SMC are a class of Monte Carlo-based algorithms, that are designed to approximate expectations w.r.t a sequence of related probability measures. These approaches have been used successfully for a wide class of applications in engineering, statistics, physics, and operations research. SMC methods are highly suited to many option pricing problems and sensitivity/Greek calculations due to the nature of the sequential simulation. However, it is seldom the case that such ideas are explicitly used in the option pricing literature. This article provides an up-to-date review of SMC methods, which are appropriate for option pricing. In addition, it is illustrated how a number of existing approaches for option pricing can be enhanced via SMC. Specifically, when pricing the arithmetic Asian option w.r.t a complex stochastic volatility model, it is shown that SMC methods provide additional strategies to improve estimation.     

用加权残余法求解含大参数的Duffing方程

本文应用加权残余法分析了含大参数的 Duffing方程 ,并得到了整个区域内 (0 <ε<∞ )一致有效的近似解 ,得到的近似周期的最大相对误差小于 7.0 % ,当参数为小量时 (ε 1 ) ,得到的近似解和摄动解完全一致 .     

Modifications of weighted Monte Carlo algorithms for nonlinear kinetic equations

Test problems for the nonlinear Boltzmann and Smoluchowski kinetic equations are used to analyze the efficiency of various versions of weighted importance modeling as applied to the evolution of multiparticle ensembles. For coagulation problems, a considerable gain in computational costs is achieved via the approximate importance modeling of the “free path” of the ensemble combined with the importance modeling of the index of a pair of interacting particles. A weighted modification of the modeling of the initial velocity distribution was found to be the most efficient for model solutions to the Boltzmann equation. The technique developed can be useful as applied to real-life coagulation and relaxation problems for which the model problems considered give approximate solutions.     

电力系统可靠性的蒙特卡洛方法

本文首先对中国科学技术大学管理科研楼电力系统可靠度评估建立了线性传感器模型。由于线性传感器可靠度评估是一个#P问题,没有多项式时间的算法。所以本文运用了蒙特卡罗方法,考虑到未加改进的蒙特卡洛方法对于解决本身可靠度很高的系统时的效率非常低,本文使用了广泛应用于网络可靠性的RVR(Recursive Variance Reduction)方法,给出了可靠度的测算结果。     

An Efficient Monte Carlo Method for Optimal Control Problems with Uncertainty

A general framework is proposed for what we call the sensitivity derivative Monte Carlo (SDMC) solution of optimal control problems with a stochastic parameter. This method employs the residual in the first-order Taylor series expansion of the cost functional in terms of the stochastic parameter rather than the cost functional itself. A rigorous estimate is derived for the variance of the residual, and it is verified by numerical experiments involving the generalized steady-state Burgers equation with a stochastic coefficient of viscosity. Specifically, the numerical results show that for a given number of samples, the present method yields an order of magnitude higher accuracy than a conventional Monte Carlo method. In other words, the proposed variance reduction method based on sensitivity derivatives is shown to accelerate convergence of the Monte Carlo method. As the sensitivity derivatives are computed only at the mean values of the relevant parameters, the related extra cost of the proposed method is a fraction of the total time of the Monte Carlo method.     

周期单元中相对论Boltzmann方程初值问题整体解的存在性

本文证明了周期单元中带有某些硬相互作用的相对论Ｂｏｌｔｚｍａｎｎ方程初值问题在初值满足质量、能量和熵有限的条件下具有一个整体温和解．     

On solving integral equations using Markov chain Monte Carlo methods

In this paper, we propose an original approach to the solution of Fredholm equations of the second kind. We interpret the standard Von Neumann expansion of the solution as an expectation with respect to a probability distribution defined on a union of subspaces of variable dimension. Based on this representation, it is possible to use trans-dimensional Markov chain Monte Carlo (MCMC) methods such as Reversible Jump MCMC to approximate the solution numerically. This can be an attractive alternative to standard Sequential Importance Sampling (SIS) methods routinely used in this context. To motivate our approach, we sketch an application to value function estimation for a Markov decision process. Two computational examples are also provided.     

多级运载火箭动态级间分离的Monte Carlo仿真

提出了研究多级运载火箭级间冷、热分离的计算公式．利用Monte Carlo仿真，考虑箭体分离时，采用远离正常设计值的参数去评估分离失败的风险．分析了各种扰动，如动态不平衡，剩余推力，分离机构引起的分离扰动，以及冷、热分离未对准的影响，得到分离箭体不会发生碰撞的条件．结果表明，当前设计满足分离要求．     

Highly nonlinear model in finance and convergence of Monte Carlo simulations

In this paper we consider the highly nonlinear model in finance proposed by Ait-Sahalia [Y. Ait-Sahalia, Testing continuous-time models of the spot interest rate, Rev. Finan. Stud. 9 (2) (1996) 385-426]. Both the drift and diffusion coefficients in this model do not obey the classical linear growth condition. To overcome the difficulties due to the highly nonlinear coefficients, we develop several new techniques to study the analytical properties of the model including the positivity and boundedness. In particular, we show that the Euler-Maruyama approximate solutions converge to the true solution in probability. The convergence result justifies clearly that the Monte Carlo simulations based on the Euler-Maruyama scheme can be used to compute the expected payoff of financial products e.g. options.     

组合KdV方程带修正函数的格子Boltzmann模型

采用一种带修正函数的新格子Boltzmann模型模拟了组合Kdv方程,分析了由此得出的迭代格式的单调性和稳定性,并且得到了格式的单调性条件.在文中的单调性条件下,迭代格式是L∞稳定的.文中的数值模拟结果表明该格式是可行的.     

Existence of Infinite Energy Solution to the Inelastic Boltzmann Equation with External Force

In this paper, the Cauchy problem for the inelastic Boltzmann equation with external force is considered in the case of initial data with infinite energy. More precisely, under the assumptions on the bicharacteristic generated by external force, we prove the global existence of solution for small initial data compared to the local Maxwellian exp{–p|x – v|²}, which has infinite mass and energy.     

Dynamically Rescaled Hamiltonian Monte Carlo for Bayesian Hierarchical Models

Dynamically rescaled Hamiltonian Monte Carlo is introduced as a computationally fast and easily implemented method for performing full Bayesian analysis in hierarchical statistical models. The method relies on introducing a modified parameterization so that the reparameterized target distribution has close to constant scaling properties, and thus is easily sampled using standard (Euclidian metric) Hamiltonian Monte Carlo. Provided that the parameterizations of the conditional distributions specifying the hierarchical model are “constant information parameterizations” (CIPs), the relation between the modified- and original parameterization is bijective, explicitly computed, and admit exploitation of sparsity in the numerical linear algebra involved. CIPs for a large catalogue of statistical models are presented, and from the catalogue, it is clear that many CIPs are currently routinely used in statistical computing. A relation between the proposed methodology and a class of explicitly integrated Riemann manifold Hamiltonian Monte Carlo methods is discussed. The methodology is illustrated on several example models, including a model for inflation rates with multiple levels of nonlinearly dependent latent variables. Supplementary materials for this article are available online.     

Multilevel Monte Carlo in approximate Bayesian computation

In the following article, we consider approximate Bayesian computation (ABC) inference. We introduce a method for numerically approximating ABC posteriors using the multilevel Monte Carlo (MLMC). A sequential Monte Carlo version of the approach is developed and it is shown under some assumptions that for a given level of mean square error, this method for ABC has a lower cost than i.i.d. sampling from the most accurate ABC approximation. Several numerical examples are given.     

Boltzmann方程的L~1稳定性

本文研究在小初值情况下Boltzmann方程经典解的L1稳定性.借助于Toscani等人所给的估计,对硬位势和软位势作了讨论,完善了[2]中关于硬球模型的结果.     

Monte Carlo optimization

Monte Carlo optimization techniques for solving mathematical programming problems have been the focus of some debate. This note reviews the debate and puts these stochastic methods in their proper perspective.     

Kac方程的解及其性质

研究Kac方程的初值问题．证明了该类方程存在唯一的全局分布解．并且使用一种新的线性化方法证明了该类方程的解具有相应的多项式衰减性．