A Probability for Classification Based on the Dirichlet Process Mixture Model

In this paper we provide an explicit probability distribution for classification purposes when observations are viewed on the real line and classifications are to be based on numerical orderings. The classification model is derived from a Bayesian nonparametric mixture of Dirichlet process model; with some modifications. The resulting approach then more closely resembles a classical hierarchical grouping rule in that it depends on sums of squares of neighboring values. The proposed probability model for classification relies on a numerical procedure based on a reversible Markov chain Monte Carlo (MCMC) algorithm for determining the probabilities. Some numerical illustrations comparing with alternative ideas for classification are provided.     

Adaptive Mixture Discriminant Analysis for Supervised Learning with Unobserved Classes

In supervised learning, an important issue usually not taken into account by classical methods is that a class represented in the test set may have not been encountered earlier in the learning phase. Classical supervised algorithms will automatically label such observations as belonging to one of the known classes in the training set and will not be able to detect new classes. This work introduces a model-based discriminant analysis method, called adaptive mixture discriminant analysis (AMDA), which can detect several unobserved groups of points and can adapt the learned classifier to the new situation. Two EM-based procedures are proposed for parameter estimation and model selection criteria are used for selecting the actual number of classes. Experiments on artificial and real data demonstrate the ability of the proposed method to deal with complex and real-world problems. The proposed approach is also applied to the detection of unobserved communities in social network analysis.     

科学解释的贝叶斯网络模型探析

解释项和被解释项之间的逻辑相关、统计相关以及因果相关等语义学模型都不能揭示科学解释的内在机制。解释必须涉及语境,必须关注解释主体。范·弗拉森和阿欣斯坦等人把解释拓展为理论、事实和语境的三元关系,但因为不是基于解释者的模型,也不能摆脱解释困境。基于贝叶斯网络的解释模型介于解释模型的语义学进路和语用学进路之间,它立足于把解释的因果关系和解释者的信念相结合,把定性解释和定量解释考虑在内从而避免科学解释陷入困境。     

项目评价系统的结构:模型与分析

从系统理论的视角,构建了一个项目评价系统的结构模型,为解释项目评价效果为什么不理想的深层原因提供了一个分析框架,也为改善项目评价的效果提供了可行的路径指引。模型的结构-功能分析分析表明,项目评价系统是一个复杂系统,项目评价主体、项目评价标准和方法、项目评价结果与项目实施主体等要素之间存在复杂的反馈回路。评价主体与项目实施主体之间的相互作用状态对项目评价系统的功能具有决定性影响。如果评价系统中存在项目实施主体对评价主体的作用,那么该系统一般存在劣性正反馈,影响评价效果。为了保证评价主体的独立性,评价主体与项目实施主体和评价客体之间不应存在利益关联。     

复杂的实在

本文认为,在复杂性研究的视野中,复杂的实在概念,其基础的哲学观点是一种"结构-关系"并存的实在论;是一种"条件-过程"实在论;是一种"历史-语境"的实在论.复杂性研究丰富了实在的概念.     

Dimensionally Reduced Model-Based Clustering Through Mixtures of Factor Mixture Analyzers

Dimensionally reduced model-based clustering methods are recently receiving a wide interest in statistics as a tool for performing simultaneously clustering and dimension reduction through one or more latent variables. Among these, Mixtures of Factor Analyzers assume that, within each component, the data are generated according to a factor model, thus reducing the number of parameters on which the covariance matrices depend. In Factor Mixture Analysis clustering is performed through the factors of an ordinary factor analysis which are jointly modelled by a Gaussian mixture. The two approaches differ in genesis, parameterization and consequently clustering performance. In this work we propose a model which extends and combines them. The proposed Mixtures of Factor Mixture Analyzers provide a unified class of dimensionally reduced mixture models which includes the previous ones as special cases and could offer a powerful tool for modelling non-Gaussian latent variables.     

大型科学仪器共享的三层次模型及实例分析

我国的大型科学仪器共享水平与发达国家相比差距很大。其中，缺乏有效的共享模型用以指导构建完善的共享系统是重要原因之一。本文基于科技资源共享三层次模型，构建了包括可见、可及、可用三个层次的大型科学仪器共享三层次模型。结合发达国家研究与实验基地大型科学仪器的共享实例对三层次中的共享内容、共享策略、预约过程、实时监控和培训等进行了细化分析。     

Bayesian Regularization for Normal Mixture Estimation and Model-Based Clustering

Normal mixture models are widely used for statistical modeling of data, including cluster analysis. However maximum likelihood estimation (MLE) for normal mixtures using the EM algorithm may fail as the result of singularities or degeneracies. To avoid this, we propose replacing the MLE by a maximum a posteriori (MAP) estimator, also found by the EM algorithm. For choosing the number of components and the model parameterization, we propose a modified version of BIC, where the likelihood is evaluated at the MAP instead of the MLE. We use a highly dispersed proper conjugate prior, containing a small fraction of one observation's worth of information. The resulting method avoids degeneracies and singularities, but when these are not present it gives similar results to the standard method using MLE, EM and BIC.     

复杂系统的迷雾

复杂系统虽是复杂性科学的研究对象,但关于它的界定却众说纷纭,充满了模糊性、含混性、矛盾性和语言迷雾,没有统一的和普适的"复杂系统";之所以如此,是因为没有大家共同接受的度量复杂和简单的标尺,不同的度量标尺将产生不同的"复杂系统".科学哲学应该清除科学语言中的迷雾.     

Finite Mixture Modeling of Gaussian Regression Time Series with Application to Dendrochronology

Finite mixture modeling is a popular statistical technique capable of accounting for various shapes in data. One popular application of mixture models is model-based clustering. This paper considers the problem of clustering regression autoregressive moving average time series. Two novel estimation procedures for the considered framework are developed. The first one yields the conditional maximum likelihood estimates which can be used in cases when the length of times series is substantial. Simple analytical expressions make fast parameter estimation possible. The second method incorporates the Kalman filter and yields the exact maximum likelihood estimates. The procedure for assessing variability in obtained estimates is discussed. We also show that the Bayesian information criterion can be successfully used to choose the optimal number of mixture components and correctly assess time series orders. The performance of the developed methodology is evaluated on simulation studies. An application to the analysis of tree ring data is thoroughly considered. The results are very promising as the proposed approach overcomes the limitations of other methods developed so far.     

A Simple Identification Proof for a Mixture of Two Univariate Normal Distributions

A simple proof of the identification of a mixture of two univariate normal distributions is given. The proof is based on the equivalence of local identification with positive definiteness of the information matrix and the equivalence of the latter to a condition on the score vector that is easily checked for this model. Two extensions using the same line of proof are also given. We would like to thank Tom Wansbeek, Michel Wedel, Arie Kapteyn, and two anonymous reviewers for helpful comments on earlier versions of this paper.     

冗余与复杂系统的演化

在分析复杂系统演化中的分叉与不可逆性的基础上．探讨了冗余之于系统演化的本体论意义和认识论意义，即冗余是系统演化实现其多样性的基本前提，也是人们把握系统演化全貌的认识论条件。本文还进一步说明了系统演化中的优劣问题等，以期给系统演化问题的研究带来一些新的理解。     

Piecewise Regression Mixture for Simultaneous Functional Data Clustering and Optimal Segmentation

This paper introduces a novel mixture model-based approach to the simultaneous clustering and optimal segmentation of functional data, which are curves presenting regime changes. The proposed model consists of a finite mixture of piecewise polynomial regression models. Each piecewise polynomial regression model is associated with a cluster, and within each cluster, each piecewise polynomial component is associated with a regime (i.e., a segment). We derive two approaches to learning the model parameters: the first is an estimation approach which maximizes the observed-data likelihood via a dedicated expectation-maximization (EM) algorithm, then yielding a fuzzy partition of the curves into K clusters obtained at convergence by maximizing the posterior cluster probabilities. The second is a classification approach and optimizes a specific classification likelihood criterion through a dedicated classification expectation-maximization (CEM) algorithm. The optimal curve segmentation is performed by using dynamic programming. In the classification approach, both the curve clustering and the optimal segmentation are performed simultaneously as the CEM learning proceeds. We show that the classification approach is a probabilistic version generalizing the deterministic K-means-like algorithm proposed in Hébrail, Hugueney, Lechevallier, and Rossi (2010). The proposed approach is evaluated using simulated curves and real-world curves. Comparisons with alternatives including regression mixture models and the K-means-like algorithm for piecewise regression demonstrate the effectiveness of the proposed approach.     

析亚里士多德：前提为必然、前提为实然的模态三段论

本文主要从亚里士多德关于—前提为必然—前提为实然的模态三段论原文出发，对其思想进行分析和整理，指出了亚里士多德主张的十三个模态有效式，并提炼出其公理化思想。在此基础上，对亚里士多德的思想加以补充，提出了第十四个模态有效式并给予证明。     

中国干细胞研究资助模式浅析

干细胞研究是当前生物医学领域最具前景的研究方向之一。经过国家973计划和国家重大科学研究计划十年部署,已经形成我国干细胞研究的基本布局,积聚了一批优秀人才,并获得了一些代表性成果。鉴于干细胞研究是一种具有明确目标导向的战略性研究领域,下一步我国应该加大对干细胞研究的支持,进一步强化干细胞国家重大科学研究计划的作用,通过加强顶层设计、改进管理模式、推动人才队伍建设和加强对外合作等方式,促进政产学研协调发展。