Sampling efficiency in Monte Carlo analyses

Stochastic processes that are sampled in Monte Carlo analyses can be so complex that sampling efficiency is difficult to attain. To handle these difficulties we introduce a model of the elements of a stochastic process which are relevant to the problem of sampling efficiency. From this model we derive a multistage estimating procedure which automatically adjusts the parameters of an efficient sampling design.     

Conditioning in Markov Chain Monte Carlo

Abstract

The so-called “Rao-Blackwellized” estimators proposed by Gelfand and Smith do not always reduce variance in Markov chain Monte Carlo when the dependence in the Markov chain is taken into account. An illustrative example is given, and a theorem characterizing the necessary and sufficient condition for such an estimator to always reduce variance is proved.     

Monte Carlo study of time asymptotics of the polarized radiation intensity

The parameters of time asymptotics of the polarized radiation intensity are estimated. Precision Monte Carlo estimates of these parameters are derived for finite medium layers by iterating the resolvent of the corresponding transfer operator with a given scattering matrix and by evaluating parametric time derivatives. The computations are performed for two versions of the problem: with a Rayleigh scattering matrix and an aerosol scattering matrix. It is shown that the asymptotics of the radiation intensity are affected by polarization, except for the spatially homogeneous problem, for which the results are obtained analytically.     

Monte Carlo Maximum Likelihood in Model-Based Geostatistics

When using a model-based approach to geostatistical problems, often, due to the complexity of the models, inference relies on Markov chain Monte Carlo methods. This article focuses on the generalized linear spatial models, and demonstrates that parameter estimation and model selection using Markov chain Monte Carlo maximum likelihood is a feasible and very useful technique. A dataset of radionuclide concentrations on Rongelap Island is used to illustrate the techniques. For this dataset we demonstrate that the log-link function is not a good choice, and that there exists additional nonspatial variation which cannot be attributed to the Poisson error distribution. We also show that the interpretation of this additional variation as either micro-scale variation or measurement error has a significant impact on predictions. The techniques presented in this article would also be useful for other types of geostatistical models.     

Population Monte Carlo Algorithm in High Dimensions

The population Monte Carlo algorithm is an iterative importance sampling scheme for solving static problems. We examine the population Monte Carlo algorithm in a simplified setting, a single step of the general algorithm, and study a fundamental problem that occurs in applying importance sampling to high-dimensional problem. The precision of the computed estimate from the simplified setting is measured by the asymptotic variance of estimate under conditions on the importance function. We demonstrate the exponential growth of the asymptotic variance with the dimension and show that the optimal covariance matrix for the importance function can be estimated in special cases.     

Implementations of the Monte Carlo EM Algorithm

The Monte Carlo EM (MCEM) algorithm is a modification of the EM algorithm where the expectation in the E-step is computed numerically through Monte Carlo simulations. The most exible and generally applicable approach to obtaining a Monte Carlo sample in each iteration of an MCEM algorithm is through Markov chain Monte Carlo (MCMC) routines such as the Gibbs and Metropolis–Hastings samplers. Although MCMC estimation presents a tractable solution to problems where the E-step is not available in closed form, two issues arise when implementing this MCEM routine: (1) how do we minimize the computational cost in obtaining an MCMC sample? and (2) how do we choose the Monte Carlo sample size? We address the first question through an application of importance sampling whereby samples drawn during previous EM iterations are recycled rather than running an MCMC sampler each MCEM iteration. The second question is addressed through an application of regenerative simulation. We obtain approximate independent and identical samples by subsampling the generated MCMC sample during different renewal periods. Standard central limit theorems may thus be used to gauge Monte Carlo error. In particular, we apply an automated rule for increasing the Monte Carlo sample size when the Monte Carlo error overwhelms the EM estimate at any given iteration. We illustrate our MCEM algorithm through analyses of two datasets fit by generalized linear mixed models. As a part of these applications, we demonstrate the improvement in computational cost and efficiency of our routine over alternative MCEM strategies.     

二重积分的蒙特卡罗数值算法

研究了二重积分的蒙特卡罗高精度求解算法,理论上对比了它们基于相同样本量的计算精度.结合MATLAB语言进行了算例分析,给出了可推广的一般化的蒙特卡罗算法程序.     

Method of Moments Using Monte Carlo Simulation

Abstract

We present a computational approach to the method of moments using Monte Carlo simulation. Simple algebraic identities are used so that all computations can be performed directly using simulation draws and computation of the derivative of the log-likelihood. We present a simple implementation using the Newton-Raphson algorithm with the understanding that other optimization methods may be used in more complicated problems. The method can be applied to families of distributions with unknown normalizing constants and can be extended to least squares fitting in the case that the number of moments observed exceeds the number of parameters in the model. The method can be further generalized to allow “moments” that are any function of data and parameters, including as a special case maximum likelihood for models with unknown normalizing constants or missing data. In addition to being used for estimation, our method may be useful for setting the parameters of a Bayes prior distribution by specifying moments of a distribution using prior information. We present two examples—specification of a multivariate prior distribution in a constrained-parameter family and estimation of parameters in an image model. The former example, used for an application in pharmacokinetics, motivated this work. This work is similar to Ruppert's method in stochastic approximation, combines Monte Carlo simulation and the Newton-Raphson algorithm as in Penttinen, uses computational ideas and importance sampling identities of Gelfand and Carlin, Geyer, and Geyer and Thompson developed for Monte Carlo maximum likelihood, and has some similarities to the maximum likelihood methods of Wei and Tanner.     

Monte Carlo solution of combinatorial optimization problems

Doklady Mathematics - A general method for solving combinatorial optimization problems based on the Metropolis algorithm is developed. The method is easy to implement, efficient, and universal. It...     

The Optimal Error of Monte Carlo Integration

The study of optimal errors of Monte Carlo methods has gained interest in recent years. Since presently no general means are available, the investigation of model problems may help one to understand the mechanisms behind them. The author provides the optimal error for the Monte Carlo integration for input data from a ball of continuous functions. As it turns out, a slight modification of the "crude Monte Carlo method" with fixed cardinality is strictly optimal even among possibly nonlinear Monte Carlo rules with varying cardinality.     

Monte Carlo simulation of boundary layer transition

A statistical method for simulating a boundary layer transition flow is proposed as based on experimental data on the kinematics and dynamics of turbulent spots (Emmons spots) on a flat plate placed in an incompressible fluid. The method determines intermittency with allowance for overlapping spots, which makes it possible to determine the forces on the plate surface and the flow field near the transition region if the mean streamwise velocity field in a developed turbulent boundary layer is known as a function of the Reynolds number. In contrast to multiparameter transition models, this approach avoids the use of nonphysical parameter values.     

Monte Carlo滤波新进展及其应用

非高斯、非线性的状态空间模型研究近年来有很多新的进展，在许多领域得到应用，其中用Monte Carlo方法进行滤波是一种简洁、方便的新方法，本文介绍这方面的新进展以及应用情况，包括本研究组的研究成果．     

On Monte Carlo methods in distributed memory systems

The way for solving a system of linear algebraic equations (SLAEs) with computers with distributed memory is presented. It is assumed that there are M computing nodes, each of which has a limited fast memory, and communication between nodes takes considerable time.If the matrix elements and the right side vectors cannot be placed in their entirety in the one node memory, the problem of using equipment efficiently between the exchange, i.e., whether each node is able to use the available information to reduce the total residual, appears. The answer to this question is negative under general assumptions on the system’s matrix and the example presented in the Appendix verifies this fact. We examine the case when the system is of sufficiently high order and it is reasonable to use the Monte Carlo method. In this case the matrix is divided between computing nodes on blocks of rows that do not overlap with the same partition into blocks of indices of rows and columns. We also consider a modification of the method of simple iteration based on this partition consisting of two nested iterative processes so that messaging between nodes takes place only in the outer iterations. This iterative process naturally results in a similar process, where the Monte Carlo method is used, and where it is not necessary to save a matrix’s full copy at each computing node. The unbiased estimations of linear algebraic equations’ solutions for the examined case are studied in the present paper. Under certain additional conditions imposed on the matrix, we prove the sufficient convergence conditions.     

Parametric weighted minimax estimates in Monte Carlo methods

In the similar trajectory method (STM), the numerical-statistical modeling of trajectories of particles (radiation quanta) is implemented by applying an auxiliary radiation model. Weighted estimates of the functionals are calculated simultaneously for a set physical parameters values. Theoretical and numerical aspects of choosing an auxiliary model with the aim of minimizing the parametric maximum of the mean-square error in weighted estimates are discussed. Previously known results concerning minimax STM algorithms are refined, and new assertions are obtained. The STM is used to numerically study the parametric dependence of the “transport approximation” error for the particle transmission, absorption, and reflection probabilities.     

Hypothesis Tests of Convergence in Markov Chain Monte Carlo

Abstract

Deciding when a Markov chain has reached its stationary distribution is a major problem in applications of Markov Chain Monte Carlo methods. Many methods have been proposed ranging from simple graphical methods to complicated numerical methods. Most such methods require a lot of user interaction with the chain which can be very tedious and time-consuming for a slowly mixing chain. This article describes a system to reduce the burden on the user in assessing convergence. The method uses simple nonparametric hypothesis testing techniques to examine the output of several independent chains and so determines whether there is any evidence against the hypothesis of convergence. We illustrate the proposed method on some examples from the literature.