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.     

作为表达的模型

通常认为,模型在科学知识的组织和获得过程中起中心作用,大多数模型以某种方式表达了它们的目标系统。本文试图探讨的问题是关于模型如何表达它的目标系统。首先本文将引入模型的表达理论必须面对的两个基本问题,接下来将试图评述理论的语义观的解决方案所面临的问题及其原因,最后本文将对在何种方向上寻求模型的表达理论提出意见。     

Multidimensional Item Response Theory Models with Collateral Information as Poisson Regression Models

Multiple choice items on tests and Likert items on surveys are ubiquitous in educational, social and behavioral science research; however, methods for analyzing of such data can be problematic. Multidimensional item response theory models are proposed that yield structured Poisson regression models for the joint distribution of responses to items. The methodology presented here extends the approach described in Anderson, Verkuilen, and Peyton (2010) that used fully conditionally specified multinomial logistic regression models as item response functions. In this paper, covariates are added as predictors of the latent variables along with covariates as predictors of location parameters. Furthermore, the models presented here incorporate ordinal information of the response options thus allowing an empirical examination of assumptions regarding the ordering and the estimation of optimal scoring of the response options. To illustrate the methodology and flexibility of the models, data from a study on aggression in middle school (Espelage, Holt, and Henkel 2004) is analyzed. The models are fit to data using SAS.     

Axiomatization and Models of Scientific Theories

In this paper we discuss two approaches to the axiomatization of scientific theories in the context of the so called semantic approach, according to which (roughly) a theory can be seen as a class of models. The two approaches are associated respectively to Suppes’ and to da Costa and Chuaqui’s works. We argue that theories can be developed both in a way more akin to the usual mathematical practice (Suppes), in an informal set theoretical environment, writing the set theoretical predicate in the language of set theory itself or, more rigorously (da Costa and Chuaqui), by employing formal languages that help us in writing the postulates to define a class of structures. Both approaches are called internal, for we work within a mathematical framework, here taken to be first-order ZFC. We contrast these approaches with an external one, here discussed briefly. We argue that each one has its strong and weak points, whose discussion is relevant for the philosophical foundations of science.     

Metric Models for Random Graphs

Many problems entail the analysis of data that are independent and identically distributed random graphs. Useful inference requires flexible probability models for such random graphs; these models should have interpretable location and scale parameters, and support the establishment of confidence regions, maximum likelihood estimates, goodness-of-fit tests, Bayesian inference, and an appropriate analogue of linear model theory. Banks and Carley (1994) develop a simple probability model and sketch some analyses; this paper extends that work so that analysts are able to choose models that reflect application-specific metrics on the set of graphs. The strategy applies to graphs, directed graphs, hypergraphs, and trees, and often extends to objects in countable metric spaces.     

Modelling Experiments as Mediating Models

Syntactic and structural models specify relationships between their constituents but cannot show what outcomes their interaction would produce over time in the world. Simulation consists in iterating the states of a model, so as to produce behaviour over a period of simulated time. Iteration enables us to trace the implications and outcomes of inference rules and other assumptions implemented in the models that make up a theory. We apply this method to experiments which we treat as models of the particular aspects of reality they are designed to investigate. Scientific experiments are constantly designed and re-designed in the context of implementation and use. They mediate between theoretical understanding and the practicalities of engaging with the empirical and social world. In order to model experiments we need to identify and represent features that all experiments have in common. We treat these features as parameters of a general model of experiment so that by varying these parameters different types of experiment can be modelled.

对运气解释模型的批判性考察

知识与运气的关系自柏拉图时代就被提起。但在当代西方知识论中,研究的重心却一直在知识的这一方,对运气的研究就没有那么深入和丰富。文章通过总结归纳学者们对运气的解释模型或观点,即重要性论题、缺乏控制力观点、概率性观点、模态解释和形而上学的解释,进行批判性考察。并借鉴其中的优点,发展出一个新的解释。     

Theory of Knowledge in System Dynamics Models

Having entered into the problem structuring methods, system dynamics (SD) is an approach, among systems’ methodologies, which claims to recognize the main structures of socio-economic behaviors. However, the concern for building or discovering strong philosophical underpinnings of SD, undoubtedly playing an important role in the modeling process, is a long-standing issue, in a way that there is a considerable debate about the assumptions or the philosophical foundations of it. In this paper, with a new perspective, we have explored theory of knowledge in SD models and found strange similarities between classic epistemological concepts such as justification and truth, and the mechanism of obtaining knowledge in SD models. In this regard, we have discussed related theories of epistemology and based on this analysis, have suggested some implications for moderating common problems in the modeling process of SD. Furthermore, this research could be considered a reword of system dynamics modeling principles in terms of theory of knowledge.     

Identifiablity of Models for Clusterwise Linear Regression

The model choice and the interpretation of the parameters are discussed as well as the use of the identifiability concept for fixed partition models. The concept is generalized to "partial identifiability".     

GME Estimation of Spatial Structural Equations Models

The objective of this paper is to develop a GME formulation for the class of spatial structural equations models (S-SEM). In this respect, two innovatory aspects are introduced: (i) the formalization of the GME estimation approach for structural equations models that account for spatial heterogeneity and spatial dependence; (ii) the extension of the methodology to a panel data framework. We also present an application of the method to real data finalized to investigate disparities of unemployment rates in OECD countries over the period 1998-2006.     

从术语学基本模型的演变看术语学的发展趋势

通过对术语学领域中不同时期的基本模型进行分析,发现包含术语、概念、客体三个元素的三角模型是最为典型的术语学理论模型,在此基础之上,不同学者根据不同研究依据、研究倾向和研究目标构建了不同形式的术语学理论模型。他们或是基于哲学、语言学、神经科学基础构建模型,或是以术语管理、术语翻译、术语工作人工智能化应用等为目标构建模型。对众多不同形式的术语模型分析最终还需要回归到几个重要概念以及这些概念之间存在的关系分析上。另一方面,也希望通过对这些模型的分析展示术语学发展的脉络和发展趋势。     

Functional PCA and Base-Line Logit Models

In many statistical applications data are curves measured as functions of a continuous parameter as time. Despite of their functional nature and due to discrete-time observation, these type of data are usually analyzed with multivariate statistical methods that do not take into account the high correlation between observations of a single curve at nearby time points. Functional data analysis methodologies have been developed to solve these type of problems. In order to predict the class membership (multi-category response variable) associated to an observed curve (functional data), a functional generalized logit model is proposed. Base-line category logit formulations will be considered and their estimation based on basis expansions of the sample curves of the functional predictor and parameters. Functional principal component analysis will be used to get an accurate estimation of the functional parameters and to classify sample curves in the categories of the response variable. The good performance of the proposed methodology will be studied by developing an experimental study with simulated and real data.     

Visual Models in Analogical Problem Solving

Visual analogy is believed to be important in human problem solving. Yet, there are few computational models of visual analogy. In this paper, we present a preliminary computational model of visual analogy in problem solving. The model is instantiated in a computer program, called Galatea, which uses a language for representing and transferring visual information called Privlan. We describe how the computational model can account for a small slice of a cognitive-historical analysis of Maxwell’s reasoning about electromagnetism.     

科学争议治理的三种模型

科学争议治理的主要挑战在于如何克服或缓解专家和公众之间的意见分歧。专家的角色依据在于其拥有特定领域的专长,公众作为争议主题的利益相关者具有声张权益的权利。我们首先讨论科学争议带来的理论挑战,然后分析三种科学争议治理的模型,即柯林斯等基于专长研究提出的知识论模型,基切尔建立在认知分工概念基础上的公共协商模型和安德森提出的公众评估专长的证词模型,最后是对这些模型规范性的几点评论。     

探索吸引资金和合作共赢的管理新模式

本文主要介绍了我省探索科学技术基金投入渠道多元化的实践经验及其给我们的启示。一、相关背景及存在的问题　　随着我国科技体制改革的不断深入,科技创新已成为科技事业发展的重要组成部分,成为实现科技产业化的创新源头。在科技部统一领导、有关部委大力支持以及地方政府直接领导下,作为国家科技创新体系重要辅助力量的地方基础研究工作,在扶持创新人才成长、储备创新技术成果、形成创新特色产业、孕育创新综合实力等诸多方面,发挥着日益显著的承上启下作用。在此背景下,源头创新的管理工作如何创新?能否适应新形势的严峻挑战?成为科技管…