计算主义思辩

本文从毕达哥拉斯的万物皆数的思想到图灵的可计算理论,再到智能计算,论述了计算主义认知观的形成和发展的全过程。从哲学、复杂性科学和生物学、计算机科学三个方面对计算主义进行了反思,倡导认知是算法不可完全的认知观。     

Markov Chain Monte Carlo in small worlds

As the number of applications for Markov Chain Monte Carlo (MCMC) grows, the power of these methods as well as their shortcomings become more apparent. While MCMC yields an almost automatic way to sample a space according to some distribution, its implementations often fall short of this task as they may lead to chains which converge too slowly or get trapped within one mode of a multi-modal space. Moreover, it may be difficult to determine if a chain is only sampling a certain area of the space or if it has indeed reached stationarity. In this paper, we show how a simple modification of the proposal mechanism results in faster convergence of the chain and helps to circumvent the problems described above. This mechanism, which is based on an idea from the field of “small-world” networks, amounts to adding occasional “wild” proposals to any local proposal scheme. We demonstrate through both theory and extensive simulations, that these new proposal distributions can greatly outperform the traditional local proposals when it comes to exploring complex heterogenous spaces and multi-modal distributions. Our method can easily be applied to most, if not all, problems involving MCMC and unlike many other remedies which improve the performance of MCMC it preserves the simplicity of the underlying algorithm.     

专利法上的抽象思想与具体技术——计算机程序算法的客体属性分析

程序算法的可专利性是计算机程序专利保护制度的一个核心问题。文章从专利客体从“产品”向“方法”拓展的历史过程出发,揭示专利法区分抽象思想和具体技术的传统标准——“物质状态改变”。文章认为,专利法区分抽象思想与具体技术的传统标准并不像诸多学者所想象的那样否定计算机程序算法的客体属性。程序算法是运行独立于人脑的物理系统(计算机)的具体方法步骤,并非抽象的思维规则。程序算法被执行后会导致传统专利法意义上的“物质状态改变”。因此,程序算法符合前述传统标准,可顺利通过客体审查。沿着这一思路对专利法的传统理论进行整理,能够最大限度地尊重专利法传统,同时又及时地消除了技术进步对客体审查理论的挑战。     

具有正确收敛性的EM算法在电子政务中的应用

将具有正确收敛性的EM算法应用到电子政务系统中,可最大限度地找到与丢失数据相似的数值,进而保证有效地进行数据挖掘.     

双权值最短路径问题的一种解决算法

物流配送方案中,费用、时间是客户最关心的两个问题。物流企业需要基于这两个客户所关心的问题,提出一套低成本的物流配送方案。本文探求使用费用、时间双权值有向图最短路径问题,解决这种配送方案,并进行算法的时间复杂度分析。     

Multivariate Life Testing in Variably Scaled Environments

This paper examines modeling and inference questions for experiments in which different subsets of a set of k possibly dependent components are tested in r different environments. In each environment, the failure times of the set of components on test is assumed to be governed by a particular type of multivariate exponential (MVE) distribution. For any given component tested in several environments, it is assumed that its marginal failure rate varies from one environment to another via a change of scale between the environments, resulting in a joint MVE model which links in a natural way the applicable MVE distributions describing component behavior in each fixed environment. This study thus extends the work of Proschan and Sullo (1976) to multiple environments and the work of Kvam and Samaniego (1993) to dependent data. The problem of estimating model parameters via the method of maximum likelihood is examined in detail. First, necessary and sufficient conditions for the identifiability of model parameters are established. We then treat the derivation of the MLE via a numerically-augmented application of the EM algorithm. The feasibility of the estimation method is demonstrated in an example in which the likelihood ratio test of the hypothesis of equal component failure rates within any given environment is carried out.     

Simulation of right and left truncated gamma distributions by mixtures

We study the properties of truncated gamma distributions and we derive simulation algorithms which dominate the standard algorithms for these distributions. For the right truncated gamma distribution, an optimal accept–reject algorithm is based on the fact that its density can be expressed as an infinite mixture of beta distribution. For integer values of the parameters, the density of the left truncated distributions can be rewritten as a mixture which can be easily generated. We give an optimal accept–reject algorithm for the other values of the parameter. We compare the efficiency of our algorithm with the previous method and show the improvement in terms of minimum acceptance probability. The algorithm proposed here has an acceptance probability which is superior to e/4.     

集配货一体化VRP问题的具记忆功能的模拟退火算法研究

针对集配货一体化车辆路径问题的特性,建立了考虑配送车辆出行固定成本、车辆出发满载系数等因素的更加符合实际的数学模型;并设计了一种带记忆功能的模拟退火算法求解模型,确保输出结果是每次寻优的最优解;最后通过算例计算,结果表明,算法求解更优,稳定性更好,计算效率也较高。     

Bayesian inference for pairwise interacting point processes

Pairwise interacting point processes are commonly used to model spatial point patterns. To perform inference, the established frequentist methods can produce good point estimates when the interaction in the data is moderate, but some methods may produce severely biased estimates when the interaction in strong. Furthermore, because the sampling distributions of the estimates are unclear, interval estimates are typically obtained by parametric bootstrap methods. In the current setting however, the behavior of such estimates is not well understood. In this article we propose Bayesian methods for obtaining inferences in pairwise interacting point processes. The requisite application of Markov chain Monte Carlo (MCMC) techniques is complicated by an intractable function of the parameters in the likelihood. The acceptance probability in a Metropolis-Hastings algorithm involves the ratio of two likelihoods evaluated at differing parameter values. The intractable functions do not cancel, and hence an intractable ratio r must be estimated within each iteration of a Metropolis-Hastings sampler. We propose the use of importance sampling techniques within MCMC to address this problem. While r may be estimated by other methods, these, in general, are not readily applied in a Bayesian setting. We demonstrate the validity of our importance sampling approach with a small simulation study. Finally, we analyze the Swedish pine sapling dataset (Strand 1972) and contrast the results with those in the literature.     

EXPONENTIAL DISPERSION MODELS AND THE GAUSS-NEWTON ALGORITHM

It is known that the Fisher scoring iteration for generalized linear models has the same form as the Gauss-Newton algorithm for normal regression. This note shows that exponential dispersion models are the most general families to preserve this form for the scoring iteration. Therefore exponential dispersion models are the most general extension of generalized linear models for which the analogy with normal regression is preserved. The multinomial distribution is used as an example.