Two-dimensional generalisations of dynamic programming for image analysis

Dynamic programming (DP) is a fast, elegant method for solving many one-dimensional optimisation problems but, unfortunately, most problems in image analysis, such as restoration and warping, are two-dimensional. We consider three generalisations of DP. The first is iterated dynamic programming (IDP), where DP is used to recursively solve each of a sequence of one-dimensional problems in turn, to find a local optimum. A second algorithm is an empirical, stochastic optimiser, which is implemented by adding progressively less noise to IDP. The final approach replaces DP by a more computationally intensive Forward-Backward Gibbs Sampler, and uses a simulated annealing cooling schedule. Results are compared with existing pixel-by-pixel methods of iterated conditional modes (ICM) and simulated annealing in two applications: to restore a synthetic aperture radar (SAR) image, and to warp a pulsed-field electrophoresis gel into alignment with a reference image. We find that IDP and its stochastic variant outperform the remaining algorithms.     

A simulation study of the Bennett test and Miller test for coefficients of variation

In the article, properties of the Bennett test and Miller test are analyzed. Assuming that the sample size is the same for each sample and considering the null hypothesis that the coefficients of variation for k populations are equal against the hypothesis that k ? 1 coefficients of variation are the same but differ from the coefficient of variation for the kth population, the empirical significance level and the power of the test are studied. Moreover, the dependence of the test statistic and the power of the test on the ratio of coefficients of variation are considered. The analyses are performed on simulated data.     

Virtual Worlds and Social Work Education

ABSTRACT

The introduction of computerised virtual worlds in the early twenty-first century was considered to be an innovation that would be widely used in higher education. There are a number of examples of virtual worlds used for higher education in medicine, nursing, and allied health, including examples in social work. The aim of this article is to explore the potential value of virtual-world technology to enhance the student learning experience for social work education in interpersonal communication skills in a virtual health centre. Findings of a study of Australian undergraduate social work students who engaged in a role play in a virtual health centre for skill development in interpersonal communication are presented. The study findings highlight issues for social work educators to consider when deciding whether or not to use virtual-world technology in social work education, and when designing further research on similar innovations.     

Simulated classical tests in multinomial probit models

This paper compares the application of different versions of the simulated counterparts of the Wald test, the score test, and the likelihood ratio test in one- and multiperiod multinomial probit models. Monte Carlo experiments show that the use of the simple form of the simulated likelihood ratio test delivers relatively robust results regarding the testing of several multinomial probit model specifications. In contrast, the inclusion of the Hessian matrix of the simulated loglikelihood function into the simulated score test and (in the multiperiod multinomial probit model) particularly the inclusion of the quasi-maximum likelihood theory into the simulated likelihood ratio test leads to substantial computational problems. The combined application of the quasi-maximum likelihood theory with the simulated Wald test or the simulated score test is not systematically superior to the application of the other versions of these two simulated classical tests either. Neither an increase in the number of observations nor in the number of random draws in the incorporated Geweke-Hajivassiliou-Keane simulator systematically lead to more precise conformities between the frequencies of type I errors and the basic significance levels. An increase in the number of observations only decreases the frequencies of type II errors, particularly regarding the simulated classical testing of multiperiod multinomial probit model specifications.     

Expert system for production manager recruitment

Abstract

Production manager recruitment poses a three-fold problem: finding a broad-knowtedged recruiter; obtaining an objective evaluation of the candidate; and the ever present risk of accepting unqualified candidates and rejecting qualified candidates A prototype expert system (ES) was developed for the purpose of testing the technical potential for computer assisted recruiting. The ES embodied a three-fold strategy: probing the candidate's past; asking questions from a business case study; and from several human-behaviour, video-taped episodes. The candidate's responses are graded, rated for competency (excellent, good, fair, poor), and weighted. A hiring action is recommended based on the composite weight. The prototype effectively demonstrated thit potential for expert system-conducted recruiting.     

A Markov Chain Monte Carlo version of the genetic algorithm Differential Evolution: easy Bayesian computing for real parameter spaces

Differential Evolution (DE) is a simple genetic algorithm for numerical optimization in real parameter spaces. In a statistical context one would not just want the optimum but also its uncertainty. The uncertainty distribution can be obtained by a Bayesian analysis (after specifying prior and likelihood) using Markov Chain Monte Carlo (MCMC) simulation. This paper integrates the essential ideas of DE and MCMC, resulting in Differential Evolution Markov Chain (DE-MC). DE-MC is a population MCMC algorithm, in which multiple chains are run in parallel. DE-MC solves an important problem in MCMC, namely that of choosing an appropriate scale and orientation for the jumping distribution. In DE-MC the jumps are simply a fixed multiple of the differences of two random parameter vectors that are currently in the population. The selection process of DE-MC works via the usual Metropolis ratio which defines the probability with which a proposal is accepted. In tests with known uncertainty distributions, the efficiency of DE-MC with respect to random walk Metropolis with optimal multivariate Normal jumps ranged from 68% for small population sizes to 100% for large population sizes and even to 500% for the 97.5% point of a variable from a 50-dimensional Student distribution. Two Bayesian examples illustrate the potential of DE-MC in practice. DE-MC is shown to facilitate multidimensional updates in a multi-chain “Metropolis-within-Gibbs” sampling approach. The advantage of DE-MC over conventional MCMC are simplicity, speed of calculation and convergence, even for nearly collinear parameters and multimodal densities.     

Markov random-field models for estimating local labour markets

Summary. This work is motivated by data on daily travel-to-work flows observed between pairs of elemental territorial units of an Italian region. The data were collected during the 1991 population census. The aim of the analysis is to partition the region into local labour markets. We present a new method for this which is inspired by the Bayesian texture segmentation approach. We introduce a novel Markov random-field model for the distribution of the variables that label the local labour markets for each territorial unit. Inference is performed by means of Markov chain Monte Carlo methods. The issue of model hyperparameter estimation is also addressed. We compare the results with those obtained by applying a classical method. The methodology can be applied with minor modifications to other data sets.     

An Approximate Bayesian Marginal Likelihood Approach for Estimating Finite Mixtures

Estimation of finite mixture models when the mixing distribution support is unknown is an important problem. This article gives a new approach based on a marginal likelihood for the unknown support. Motivated by a Bayesian Dirichlet prior model, a computationally efficient stochastic approximation version of the marginal likelihood is proposed and large-sample theory is presented. By restricting the support to a finite grid, a simulated annealing method is employed to maximize the marginal likelihood and estimate the support. Real and simulated data examples show that this novel stochastic approximation and simulated annealing procedure compares favorably with existing methods.     

A classification approach based on the outranking model for multiple criteria ABC analysis

The multiple criteria ABC analysis is widely used in inventory management, and it can help organizations to assign inventory items into different classes with respect to several evaluation criteria. Many approaches have been proposed in the literature for addressing such a problem. However, most of these approaches are fully compensatory in multiple criteria aggregation. This means that an item scoring badly on one or more key criteria could be placed in good classes because these bad performances could be compensated by other criteria. Thus, it is necessary to consider the non-compensation in the multiple criteria ABC analysis. To the best of our knowledge, the ABC classification problem with non-compensation among criteria has not been studied sufficiently. We thus propose a new classification approach based on the outranking model to cope with such a problem in this paper. However, the relational nature of the outranking model makes the search for the optimal classification solution a complex combinatorial optimization problem. It is very time-consuming to solve such a problem using mathematical programming techniques when the inventory size is large. Therefore, we combine the clustering analysis and the simulated annealing algorithm to search for the optimal classification. The clustering analysis groups similar inventory items together and builds up the hierarchy of clusters of items. The simulated annealing algorithm searches for the optimal classification on different levels of the hierarchy. The proposed approach is illustrated by a practical example from a Chinese manufacturer. Furthermore, we validate the performance of the approach through experimental investigation on a large set of artificially generated data at the end of the paper.     

论工资率提高、通货膨胀与人民生活水平的改善——基于合肥市的实证研究

工资和物价水平是关乎民生的两个重要研究课题,稳定物价水平也是宏观经济政策的重要内容之一。随着国内物价水平不断上涨,通货膨胀预期不断递增,缓解人民生活压力和改善人民生活水平已经成为当前宏观调控的主要任务。对于通货膨胀的解释历来存在需求拉动型和成本推动型之争,对通货膨胀的不同解释意味着不同的宏观经济政策选择。利用构建联立方程组模型,将工资率和通货膨胀纳入同一个分析框架,模拟分析工资率与价格水平的之间关系。基于合肥市各部门相关数据的模拟结果显示,提高工资率和通货膨胀之间有一定的相关关系,这意味着通货膨胀包含工资成本推动因素,但是,模拟结果还显示工资的提高对经济发展的促进作用大于对价格的推动作用。