谈心理训练对跳远助跑准确性的影响

通过对中北大学校代表队跳远运动员助跑的分析研究,发现在跳远运动技术不断发展和提高的今天,心理训练方法在跳远训练中的运用已成为提高跳远成绩的主要手段之一.自我暗示结合标记跑可以在很大程度上提高运动员的助跑准确性.所以在今后的跳远训练过程中我们要避免训练方式单一化,应把心理训练更多地融入日常训练之中,这样才有利于运动员在比赛中创造更理想的成绩.     

Computational methods for complex stochastic systems: a review of some alternatives to MCMC

We consider analysis of complex stochastic models based upon partial information. MCMC and reversible jump MCMC are often the methods of choice for such problems, but in some situations they can be difficult to implement; and suffer from problems such as poor mixing, and the difficulty of diagnosing convergence. Here we review three alternatives to MCMC methods: importance sampling, the forward-backward algorithm, and sequential Monte Carlo (SMC). We discuss how to design good proposal densities for importance sampling, show some of the range of models for which the forward-backward algorithm can be applied, and show how resampling ideas from SMC can be used to improve the efficiency of the other two methods. We demonstrate these methods on a range of examples, including estimating the transition density of a diffusion and of a discrete-state continuous-time Markov chain; inferring structure in population genetics; and segmenting genetic divergence data.     

Estimation of parameters in incomplete data models defined by dynamical systems

Parametric incomplete data models defined by ordinary differential equations (ODEs) are widely used in biostatistics to describe biological processes accurately. Their parameters are estimated on approximate models, whose regression functions are evaluated by a numerical integration method. Accurate and efficient estimations of these parameters are critical issues. This paper proposes parameter estimation methods involving either a stochastic approximation EM algorithm (SAEM) in the maximum likelihood estimation, or a Gibbs sampler in the Bayesian approach. Both algorithms involve the simulation of non-observed data with conditional distributions using Hastings–Metropolis (H–M) algorithms. A modified H–M algorithm, including an original local linearization scheme to solve the ODEs, is proposed to reduce the computational time significantly. The convergence on the approximate model of all these algorithms is proved. The errors induced by the numerical solving method on the conditional distribution, the likelihood and the posterior distribution are bounded. The Bayesian and maximum likelihood estimation methods are illustrated on a simulated pharmacokinetic nonlinear mixed-effects model defined by an ODE. Simulation results illustrate the ability of these algorithms to provide accurate estimates.     

求解作业车间调度问题的一种自适应遗传算法

针对作业车间调度问题,提出了最小化空闲时间的处理过程及其变异算子,设计了一种自适应遗传算法.该算法根据个体的特征确定交叉和变异次数,并根据种群特征不断修正种群.经典的调度基准问题测试表明:自适应措施能够有效保持种群的多样性,可以采用非常小的种群规模;最小化空闲时间的变异算子缩小了算法的搜索空间,大大提高了搜索效率.     

Exploring quasi Monte Carlo for marginal density approximation

We first review quasi Monte Carlo (QMC) integration for approximating integrals, which we believe is a useful tool often overlooked by statistics researchers. We then present a manually-adaptive extension of QMC for approximating marginal densities when the joint density is known up to a normalization constant. Randomization and a batch-wise approach involving (0,s)-sequences are the cornerstones of our method. By incorporating a variety of graphical diagnostics the method allows the user to adaptively allocate points for joint density function evaluations. Through intelligent allocation of resources to different regions of the marginal space, the method can quickly produce reliable marginal density approximations in moderate dimensions. We demonstrate by examples that adaptive QMC can be a viable alternative to the Metropolis algorithm.     

Bayesian semiparametric inference for the accelerated failure-time model

Bayesian semiparametric inference is considered for a loglinear model. This model consists of a parametric component for the regression coefficients and a nonparametric component for the unknown error distribution. Bayesian analysis is studied for the case of a parametric prior on the regression coefficients and a mixture-of-Dirichlet-processes prior on the unknown error distribution. A Markov-chain Monte Carlo (MCMC) method is developed to compute the features of the posterior distribution. A model selection method for obtaining a more parsimonious set of predictors is studied. The method adds indicator variables to the regression equation. The set of indicator variables represents all the possible subsets to be considered. A MCMC method is developed to search stochastically for the best subset. These procedures are applied to two examples, one with censored data.     

一种改进的遗传算法进化有限状态机

提出了一种改进的遗传算法,针对有限状态机中输出矢量与状态转移相关的特性,将配置有限状态机的染色体分解为状态转移基因和输出矢量基因进行分阶段的进化实验。实验结果表明同传统的进化算法相比,这种分阶段进化的遗传算法降低了有限状态机进化过程中的复杂度,有效地提高了进化性能。     

A stochastic approximation algorithm for maximum-likelihood estimation with incomplete data

We propose a new stochastic approximation (SA) algorithm for maximum-likelihood estimation (MLE) in the incomplete-data setting. This algorithm is most useful for problems when the EM algorithm is not possible due to an intractable E-step or M-step. Compared to other algorithm that have been proposed for intractable EM problems, such as the MCEM algorithm of Wei and Tanner (1990), our proposed algorithm appears more generally applicable and efficient. The approach we adopt is inspired by the Robbins-Monro (1951) stochastic approximation procedure, and we show that the proposed algorithm can be used to solve some of the long-standing problems in computing an MLE with incomplete data. We prove that in general O(n) simulation steps are required in computing the MLE with the SA algorithm and O(n log n) simulation steps are required in computing the MLE using the MCEM and/or the MCNR algorithm, where n is the sample size of the observations. Examples include computing the MLE in the nonlinear error-in-variable model and nonlinear regression model with random effects.     

一种新的智能内窥镜导航系统

本文介绍了一种新的智能内窥镜导航系统。该系统用于实现内窥镜的智能导航介入。其中导航方法是基于视觉传感器和触觉传感器的多信息融合的方法。在视觉传感器中采用了一种基于自适应阈值图像分割算法的寻径方法,该方法采用加速的最大类间方差法进行阈值分割。在触觉传感器中采用了基于MEMS技术的硅微触觉传感器,四个硅微触觉传感器被封装在内窥镜头部表面,可以获得内窥镜头部全方向的压力。本文通过实验验证了该导航系统的可行性及可靠性,该方法的应用弥补了传统方法中因暗区不明显而无法引导内窥镜的不足,提高了内窥镜导航的精度和可靠性。     

Bayesian analysis of human movement curves

Summary.  We consider the Bayesian analysis of human movement data, where the subjects perform various reaching tasks. A set of markers is placed on each subject and a system of cameras records the three-dimensional Cartesian co-ordinates of the markers during the reaching movement. It is of interest to describe the mean and variability of the curves that are traced by the markers during one reaching movement, and to identify any differences due to covariates. We propose a methodology based on a hierarchical Bayesian model for the curves. An important part of the method is to obtain identifiable features of the movement so that different curves can be compared after temporal warping. We consider four landmarks and a set of equally spaced pseudolandmarks are located in between. We demonstrate that the algorithm works well in locating the landmarks, and shape analysis techniques are used to describe the posterior distribution of the mean curve. A feature of this type of data is that some parts of the movement data may be missing—the Bayesian methodology is easily adapted to cope with this situation.