正态双边可靠性的一种工程近似计算

本文提出了一种关于正态单元双边可靠性的Bayes下限近似计算方法,与目前常采用的经典查表法等相比,该方法不仅使用方便,而且具有较高的精度,因此,具有重要工程应用价值。     

方差缩减技巧与拟蒙特卡罗方法

"拟蒙特卡罗方法"是蒙特卡罗方法的确定性变形。方差缩减技巧被广泛用于提高蒙特卡罗方法的效率。本文探讨这此些技巧的确定性变形用于"拟随机"情形以减小变差（提高"拟蒙特卡罗方法"的效率的）的可能性。构造了一系列对某一函数集会精确成立的"拟随机"积分公式。     

拟蒙特卡罗法在亚洲期权定价中的应用

亚洲期权是场外交易中几种最受欢迎的新型期权之一,但它的价格却没有解析表达式,到目前为止,亚洲期权的定价仍是个公开问题.本文采用拟蒙特卡罗法中的Halton序列来估计它的价格,数值结果表明当观察点的个数N13时,它比蒙特卡罗法要好.本文还利用MATLAB程序生成了随机Halton序列,并将它与控制变量法结合起来估计亚洲期权的价格,估计值标准差的比较表明它在大多情况下比相应的蒙特卡罗法的估计效果要好.     

利用蒙特卡罗方法求解数值积分

本文介绍了蒙特卡罗方法的主要思想和理论基础,以一维定积分与二维定积分为例,应用蒙特卡罗方法借助R语言模拟计算定积分的值,并给出结果的相对误差与样本量的关系.     

蒙特卡罗方法在原子间多体势拟合中的应用

蒙特卡罗方法是求解非线性方程组的一种有效的方法.我们采用F inn is-S incla ir型的嵌入原子势,并运用蒙特卡罗方法拟合了金属T a的平衡晶格常数、结合能、弹性常数、空位形成能,给出了此元素的多体势函数的参数.     

蒙特卡罗仿真在船用核动力装置可靠性分析中的应用

如何推断系统的故障概率,是目前可靠性工程领域的一个重要问题.而对具有动态随机性故障的可修系统采用静态近似处理,经常导致计算的可靠性指标与实际情况相差甚远,采用蒙特卡罗方法产生等价于船用核动力系统基本部件故障率的随机数,代入到仿真模型中,经过逻辑运算得到等价于系统故障概率的随机数,对多次仿真得到的数据进行统计推断,便得到系统故障的概率分布及相应的置信区间.此方法计算结果精度高,对船用核动力装置的可靠性分析有重要意义.     

治愈率模型在屏蔽数据可靠性分析中的应用研究

针对传统方法中的不足,在引入标准治愈率模型的基础上,提出在屏蔽数据可靠性分析中应用一种扩展的治愈率模型的建模方法;分析证明了利用该方法进行建模分析时仅需对模型作较少的前提假设,在信息不足的情况下能够识别出伴随变量对系统寿命分布的影响,进而有效提高模型估计的稳健性.通过运用基于Gibbs抽样的MCMC方法动态模拟出相关参数后验分布的马尔可夫链,给出随机截尾条件下模型参数的贝叶斯估计;实例分析的结果,证明了该模型在可靠性应用中的直观性与有效性.     

浅谈蒙特卡罗法在概率统计教学中的应用

在概率统计教学中利用蒙特卡罗法设计适当的模拟实验,结合多媒体在课堂教学中实时演示,能很好地加深学生对概率统计概念、思想的理解,进而培养学生的统计思维能力.     

拟蒙特卡罗方法用于估计多元回归函数

拟蒙特卡罗（QMC）方法被广泛用于解决数值分析和统计学中的各种问题，比如数值积分，最优化，试验设计，随机过程的模拟等．本文研究该方法在估计多元回归函数中的应用．证明了，在相当一般的条件下，均匀设计（或者，“代表点设计”）与回归函数傅里叶系数的QMC估计（对应地，使用拟随机重要性抽样的QMC估计）一起，构成一个回归函数的渐近最优投影估计．     

Weibull小样本失效数据的贝叶斯可靠性分析

随着高性能复杂系统日益向“高质量、长寿命、小子样”等方向发展,在可靠性试验中经常出现小样本失效数据,传统的基于大样本的产品可靠性评估已不能满足试验要求.在两参数Weibull分布模型的基础上,对原始小样本数据采用灰色估计进行两参数估计,再利用蒙特卡洛方法进行随机抽样并将抽样结果纳入贝叶斯先验信息,通过贝叶斯公式得到后验分布,最终计算得到产品可靠度、失效概率、失效率等结果.最后经实例验证了该方法的有效性.     

Fast Hamiltonian Monte Carlo Using GPU Computing

In recent years, the Hamiltonian Monte Carlo (HMC) algorithm has been found to work more efficiently compared to other popular Markov chain Monte Carlo (MCMC) methods (such as random walk Metropolis–Hastings) in generating samples from a high-dimensional probability distribution. HMC has proven more efficient in terms of mixing rates and effective sample size than previous MCMC techniques, but still may not be sufficiently fast for particularly large problems. The use of GPUs promises to push HMC even further greatly increasing the utility of the algorithm. By expressing the computationally intensive portions of HMC (the evaluations of the probability kernel and its gradient) in terms of linear or element-wise operations, HMC can be made highly amenable to the use of graphics processing units (GPUs). A multinomial regression example demonstrates the promise of GPU-based HMC sampling. Using GPU-based memory objects to perform the entire HMC simulation, most of the latency penalties associated with transferring data from main to GPU memory can be avoided. Thus, the proposed computational framework may appear conceptually very simple, but has the potential to be applied to a wide class of hierarchical models relying on HMC sampling. Models whose posterior density and corresponding gradients can be reduced to linear or element-wise operations are amenable to significant speed ups through the use of GPUs. Analyses of datasets that were previously intractable for fully Bayesian approaches due to the prohibitively high computational cost are now feasible using the proposed framework.     

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。     

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.     

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.     

Noisy Hamiltonian Monte Carlo for Doubly Intractable Distributions

Hamiltonian Monte Carlo (HMC) has been progressively incorporated within the statistician’s toolbox as an alternative sampling method in settings when standard Metropolis–Hastings is inefficient. HMC generates a Markov chain on an augmented state space with transitions based on a deterministic differential flow derived from Hamiltonian mechanics. In practice, the evolution of Hamiltonian systems cannot be solved analytically, requiring numerical integration schemes. Under numerical integration, the resulting approximate solution no longer preserves the measure of the target distribution, therefore an accept–reject step is used to correct the bias. For doubly intractable distributions—such as posterior distributions based on Gibbs random fields—HMC suffers from some computational difficulties: computation of gradients in the differential flow and computation of the accept–reject proposals poses difficulty. In this article, we study the behavior of HMC when these quantities are replaced by Monte Carlo estimates. Supplemental codes for implementing methods used in the article are available online.     

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.     

Adaptive Sequential Monte Carlo for Multiple Changepoint Analysis

Process monitoring and control requires the detection of structural changes in a data stream in real time. This article introduces an efficient sequential Monte Carlo algorithm designed for learning unknown changepoints in continuous time. The method is intuitively simple: new changepoints for the latest window of data are proposed by conditioning only on data observed since the most recent estimated changepoint, as these observations carry most of the information about the current state of the process. The proposed method shows improved performance over the current state of the art. Another advantage of the proposed algorithm is that it can be made adaptive, varying the number of particles according to the apparent local complexity of the target changepoint probability distribution. This saves valuable computing time when changes in the changepoint distribution are negligible, and enables rebalancing of the importance weights of existing particles when a significant change in the target distribution is encountered. The plain and adaptive versions of the method are illustrated using the canonical continuous time changepoint problem of inferring the intensity of an inhomogeneous Poisson process, although the method is generally applicable to any changepoint problem. Performance is demonstrated using both conjugate and nonconjugate Bayesian models for the intensity. Appendices to the article are available online, illustrating the method on other models and applications.     

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。     

Unbiased multi-index Monte Carlo

We introduce a new class of Monte Carlo-based approximations of expectations of random variables such that their laws are only available via certain discretizations. Sampling from the discretized versions of these laws can typically introduce a bias. In this paper, we show how to remove that bias, by introducing a new version of multi-index Monte Carlo (MIMC) that has the added advantage of reducing the computational effort, relative to i.i.d. sampling from the most precise discretization, for a given level of error. We cover extensions of results regarding variance and optimality criteria for the new approach. We apply the methodology to the problem of computing an unbiased mollified version of the solution of a partial differential equation with random coefficients. A second application concerns the Bayesian inference (the smoothing problem) of an infinite-dimensional signal modeled by the solution of a stochastic partial differential equation that is observed on a discrete space grid and at discrete times. Both applications are complemented by numerical simulations.