A new strategy for speeding Markov chain Monte Carlo algorithms

Markov chain Monte Carlo (MCMC) methods have become popular as a basis for drawing inference from complex statistical models. Two common difficulties with MCMC algorithms are slow mixing and long run-times, which are frequently closely related. Mixing over the entire state space can often be aided by careful tuning of the chain's transition kernel. In order to preserve the algorithm's stationary distribution, however, care must be taken when updating a chain's transition kernel based on that same chain's history. In this paper we introduce a technique that allows the transition kernel of the Gibbs sampler to be updated at user specified intervals, while preserving the chain's stationary distribution. This technique seems to be beneficial both in increasing efficiency of the resulting estimates (via Rao-Blackwellization) and in reducing the run-time. A reinterpretation of the modified Gibbs sampling scheme introduced in terms of auxiliary samples allows its extension to the more general Metropolis-Hastings framework. The strategies we develop are particularly helpful when calculation of the full conditional (for a Gibbs algorithm) or of the proposal distribution (for a Metropolis-Hastings algorithm) is computationally expensive. Partial financial support from FAR 2002-3, University of Insubria is gratefully acknowledged.     

新型免疫克隆算法在永磁无刷直流电机系统中的应用

为了提高电机控制系统的动态响应和稳定性,提出于一种基于对抗制的优化免疫克隆算法,引入联赛对抗的机 制,在抗体更新的过程中,产生多组具有指向性的抗体,按赛制规则淘汰筛选,以获得最优的PID控制器参数。在永磁无 刷直流电机系统下的仿真结果表明：与一般PID控制器相比,免疫克隆算法优化控制下的永磁无刷直流电机系统具有较 好的动态特性和稳定性能。     

Handling the Label Switching Problem in Latent Class Models Via the ECR Algorithm

Latent class models (LCMs) are specific cases of mixture models. Under a Bayesian setup, the symmetric posterior distribution of these models leads Markov chain Monte Carlo (MCMC) methods to suffer from the so-called label switching problem. In this article, we treat the corresponding MCMC outputs using a recent approach, namely, the Equivalence Classes Representative (ECR) algorithm and conclude that it can effectively solve the label switching problem by considering several examples of LCMs, such as mixtures of regressions, hidden Markov models, and Markov random fields. Moreover, the superiority of this method over other approaches becomes apparent.     

改进的Wiener滤波在图像恢复中的应用简

在Wiener滤波进行图像恢复时,利用遗传算法（GA,Genetic Algorithm）全局和局部搜索力强的优势,搜索Wiener滤波的信噪比最优Г.为了得到最优Г值,利用峰值信噪比（PSNR,Peak Signal-to-Noise Ratio）作为图像恢复质量评价指标,对经过处理的LIVE数据库图像、IKONOS、ZY-3卫星图像进行实验.结果表明：无论图像噪声大小,该方法都可以找到Wiener滤波的最优Г值.     

Comparison of Two Methods for Calculating the Partition Functions of Various Spatial Statistical Models

Likelihood computation in spatial statistics requires accurate and efficient calculation of the normalizing constant (i.e. partition function) of the Gibbs distribution of the model. Two available methods to calculate the normalizing constant by Markov chain Monte Carlo methods are compared by simulation experiments for an Ising model, a Gaussian Markov field model and a pairwise interaction point field model.     

A hybrid Markov chain for the Bayesian analysis of the multinomial probit model

Bayesian inference for the multinomial probit model, using the Gibbs sampler with data augmentation, has been recently considered by some authors. The present paper introduces a modification of the sampling technique, by defining a hybrid Markov chain in which, after each Gibbs sampling cycle, a Metropolis step is carried out along a direction of constant likelihood. Examples with simulated data sets motivate and illustrate the new technique. A proof of the ergodicity of the hybrid Markov chain is also given.     

基于块状bootstrap技术的Bagging Trees集成算法研究

集成算法已经成为机器学习研究的一大热点,已有许多改进的集成算法,但对"病态"数据的集成研究并不常见。本文通过对一海藻繁殖案例的研究,提出了一种基于块状bootstrap技术的集成算法,并将其与几种常用的集成算法比较研究得出,在对于一些"病态"数据而言,该算法往往比其它算法具有更小的模型推广误差和更高的预测精度的优点。     

Density Estimation for the Metropolis–Hastings Algorithm

Abstract. Kernel density estimation is an important tool in visualizing posterior densities from Markov chain Monte Carlo output. It is well known that when smooth transition densities exist, the asymptotic properties of the estimator agree with those for independent data. In this paper, we show that because of the rejection step of the Metropolis–Hastings algorithm, this is no longer true and the asymptotic variance will depend on the probability of accepting a proposed move. We find an expression for this variance and apply the result to algorithms for automatic bandwidth selection.     

浅析21世纪中国劳动力的多样性管理

文章通过分析21世纪中国劳动力市场的现状,以及运用遗传算法对多样性管理的绩效进行测量,阐述了中国劳动力多样性管理的重要性和必要性,并在对现状中出现的问题深入分析研究的基础上提出了解决的办法和途径。     

A multiobjective genetic algorithm for job shop scheduling

In this paper, a Multi Objective Genetic Algorithm (MOGA) is proposed to derive the optimal machine-wise priority dispatching rules ( pdrs ) to resolve the conflict among the contending jobs in the Giffler and Thompson (GT) procedure applied for job shop problems. The performance criterion considered is the weighed sum of the multiple objectives minimization of makespan, minimization of total idle time of machines and minimization of total tardiness. The weights assigned for combining the objectives into a scalar fitness function are not constant. They are specified randomly for each evaluation. This in turn leads to the multidirectional search in the proposed MOGA, which in turn mitigates the solution being entrapped in local minima. The applicability and usefulness of the proposed methodology for the scheduling of job shops is illustrated with 28 benchmark problems available in the open literature.