Active learning? not with my syllabus!

We describe an approach to teaching probability that minimizes the amount of class time spent on the topic while also providing a meaningful (dice‐rolling) activity to get students engaged. The activity, which has a surprising outcome, illustrates the basic ideas of informal probability and how probability is used in statistical inference.     

归纳问题和卡尔纳普的解答

休谟的怀疑主义不仅终结了理性主义,也终结了经验主义。休谟在《人性论》中对因果关系理论进行了论证,其中包括三个前提条件和一个原则,使归纳问题的本质得到说明。归纳问题使归纳推理的合法性受到挑战,后来的哲学家们对此纷纷讨论,其中卡尔纳普的概率逻辑方案备受关注。卡尔纳普用演绎方法研究归纳过程,引进了概念确证度或说是验证度。通过论证,卡尔纳普把逻辑地确定概率P的问题转换成确定状态描述的概率,使归纳问题得到解决。然而,用演绎的方法来研究非演绎性的归纳逻辑是不可取的。     

回溯推理探赜

回溯推理是一种在司法实践中被广泛运用的或然性推理。它是根据推导关系.相对于被一定推理者所掌握但在推理中省略掉的背景知识,由证据推测假说的推理,它的前提只为人们在某种程度上置信其结论提供证据支持。回溯推理的前提与结论之间的证据支持关系可用概率理论进行刻画。实践中,回溯推理主要有两种类型和五种模式。     

提高体育统计中统计推断应用水平的关键：正确认识小概率事件原则

本文讨论了小概率事件原则在体育统计的学习和应用过程中的重要作用。指出提高我国体育统计中统计推断应用的重要途径是正确认识和理解统计推断的基本思想———小概率事件原则。据此以提高学生统计推断方法及其应用水平     

刑事侦查中的回溯推理探微——兼与倪荫林先生商榷

回溯推理是一种在侦查实践中被广泛运用的或然性推理。它有自己特定的内涵,应予正确界定。为使之能在侦查实践中被正确运用,应为其建立正确的逻辑模式,并按强度进行区分。     

Sample Selection in Randomized Experiments: A New Method Using Propensity Score Stratified Sampling

Abstract

Randomized experiments are often seen as the “gold standard” for causal research. Despite the fact that experiments use random assignment to treatment conditions, units are seldom selected into the experiment using probability sampling. Very little research on experimental design has focused on how to make generalizations to well-defined populations or on how units should be selected into an experiment to facilitate generalization. This article addresses the problem of sample selection in experiments by providing a method for selecting the sample so that the population and sample are similar in composition. The method begins by requiring that the inference population and eligibility criteria for the study are well defined before study recruitment begins. When the inference population and population of eligible units differs, the article provides a method for sample recruitment based on stratified selection on a propensity score. The article situates the problem within the example of how to select districts for two scale-up experiments currently in recruitment.     

假言+选言推理的逻辑构造,判定和使用

“假言＋选言推理”是以假言命题和选言命题为前提的组合推理。根据假言、选言全中构造出１２种推理模式。采用归谬赋值法,对其有效性进行判定“Ｎ难推理”便是这种推理的语用形式。     

Modeling Diagnostic Assessments with Bayesian Networks

This paper defines Bayesian network models and examines their applications to IRT-based cognitive diagnostic modeling. These models are especially suited to building inference engines designed to be synchronous with the finer grained student models that arise in skills diagnostic assessment. Aspects of the theory and use of Bayesian network models are reviewed, as they affect applications to diagnostic assessment. The paper discusses how Bayesian network models are set up with expert information, improved and calibrated from data, and deployed as evidence-based inference engines. Aimed at a general educational measurement audience, the paper illustrates the flexibility and capabilities of Bayesian networks through a series of concrete examples, and without extensive technical detail. Examples are provided of proficiency spaces with direct dependencies among proficiency nodes, and of customized evidence models for complex tasks. This paper is intended to motivate educational measurement practitioners to learn more about Bayesian networks from the research literature, to acquire readily available Bayesian network software, to perform studies with real and simulated data sets, and to look for opportunities in educational settings that may benefit from diagnostic assessment fueled by Bayesian network modeling.     

A typology of causal models for plate tectonics: Inferential power and barriers to understanding

Forty-seven fifth grade students (40 group-tested and 7 individually interviewed) read a text describing plate tectonics. At four points they drew diagrams of the spatial, causal, and dynamic processes inside the earth. These diagrams along with students' corresponding explanations, think-aloud protocols (for those individually interviewed), and answers to inference questions were analysed in order to characterize students' models of the interior of the earth, and models of its causal and dynamic processes. Types and characteristics of models, and reasoning associated with them are presented. Additionally, data from two exemplary students are presented as case studies. One student has considerable misunderstandings regarding both her understanding of the spatial layout of the interior of the earth and its causal mechanisms. The second student is more typical in terms of his initial models, but makes large gains in revising his understanding about the causal and dynamic processes inside the earth. In both cases, data are used to infer how each student used their diagrams as artefacts for externalizing knowledge, inference making, and model-revision.     

随机变量分布函数的讨论

当二维连续型随机变量的概率密度为常数时,给出了求其分布函数的简明方法.由于降低了计算过程的复杂性,使计算过程更严谨、清晰、简单.     

Technology‐enhanced Interactive Teaching of Marginal,Joint and Conditional Probabilities: The Special Case of Bivariate Normal Distribution

Data analysis requires subtle probability reasoning to answer questions like What is the chance of event A occurring, given that event B was observed? This generic question arises in discussions of many intriguing scientific questions such as What is the probability that an adolescent weighs between 120 and 140 pounds given that they are of average height? and What is the probability of (monetary) inflation exceeding 4% and housing price index below 110? To address such problems, learning some applied, theoretical or cross‐disciplinary probability concepts is necessary. Teaching such courses can be improved by utilizing modern information technology resources. Students' understanding of multivariate distributions, conditional probabilities, correlation and causation can be significantly strengthened by employing interactive web‐based science educational resources. Independent of the type of a probability course (e.g. majors, minors or service probability course, rigorous measure‐theoretic, applied or statistics course) student motivation, learning experiences and knowledge retention may be enhanced by blending modern technological tools within the classical conceptual pedagogical models. We have designed, implemented and disseminated a portable open‐source web‐application for teaching multivariate distributions, marginal, joint and conditional probabilities using the special case of bivariate Normal distribution. A real adolescent height and weight dataset is used to demonstrate the classroom utilization of the new web‐application to address problems of parameter estimation, univariate and multivariate inference.     

Digital Module 06: Bayesian Psychometrics—Posterior Predictive Model Checking https://ncme.elevate.commpartners.com

Drawing valid inferences from modern measurement models is contingent upon a good fit of the data to the model. Violations of model‐data fit have numerous consequences, limiting the usefulness and applicability of the model. As Bayesian estimation is becoming more common, understanding the Bayesian approaches for evaluating model‐data fit models is critical. In this instructional module, Allison Ames and Aaron Myers provide an overview of Posterior Predictive Model Checking (PPMC), the most common Bayesian model‐data fit approach. Specifically, they review the conceptual foundation of Bayesian inference as well as PPMC and walk through the computational steps of PPMC using real‐life data examples from simple linear regression and item response theory analysis. They provide guidance for how to interpret PPMC results and discuss how to implement PPMC for other model(s) and data. The digital module contains sample data, SAS code, diagnostic quiz questions, data‐based activities, curated resources, and a glossary.     

An example of Bayesian inference in thermal sciences

This article explains the method of Bayesian inference in the estimation of parameters. An account of how Bayesian inference can beadapted in a simple case of parameter retrieval in thermal sciences is explained.     

工作流响应时间概率分布分析

提出了一种基于流体随机Petri网的工作流响应时间概率分布计算方法.首先讨论了利用随机工作流网建模的一些相关问题,然后描述了如何将随机工作流网模型转化为流体随机Petri网模型,最后给出了该种流体随机Petri网模型的动态方程,说明工作流响应时间的概率分布可直接由流体随机Petri网模型的暂态解得到.该方法对工作流模型的结构没有提出任何限制,且工作流任务的处理时间可取任意概率分布,通过递归地使用网化简技术可有效地处理大型工作流模型.     

基于心理模型的词汇推理

心理模型是心智对实在的或想象的情况的表征,是人类进行推理的常规形式。词汇推理也是以心理模型为推理基础的、在大脑中建立与逐步完善一个包括二语词汇的语义、句法、形态等信息的词汇推理机制。因此,词汇推理与心理模型是互生互存的,又是动态生成的,而且推理过程是以心理模型为基础同时又是建构相关词汇的心理模型过程。     

基于非参数贝叶斯推断的MPSK信号调制识别(英文)

依据星座图采用非参数贝叶斯方法对多元相移键控(MPSK)信号进行调制识别.将未知信噪比(SNR)水平的MPSK信号看成复平面内多个未知均值和方差的高斯分布依照一定的比例混合而成,利用非参数贝叶斯推断方法进行密度估计,实现对MPSK信号分类目的.推断过程中,引入Dirichlet过程作为混合比例因子的先验分布,结合正态逆Wishart(NIW)分布作为均值和方差的先验分布,根据接收信号,利用Gibbs采样的MCMC(Monte Carlo Markov chain)随机采样算法,不断调整混合比例因子、均值和方差.通过多次迭代,得到对调制信号的密度估计.仿真表明,在SNR>5 dB,码元数目大于1 600时,2/4/8PSK的识别率超过了95%.     

基于贝叶斯推断的多层软测量建模在丁醇发酵中的应用

针对丁醇生产过程中发酵产物品质参量难以实时测量,现有测量方法精度不高、测量结果受不确定因素影响较大的问题,提出一种基于贝叶斯推断和支持向量回归（Support vector machine regression,SVR）的多层软测量建模方法。首先应用贝叶斯推断计算后验概率、筛选偏置数据,并对偏置数据校准,建立第一层SVR模型;然后利用贝叶斯推断进行二次校准,建立第二层SVR模型,对第一层SVR模型输出进行修正,得到最终预测结果,克服干扰和偏差引起的模型不准确问题。将基于贝叶斯推断的多层支持向量回归（Bi-SVR）预测模型应用于丁醇发酵过程,仿真及实验结果表明,相较于传统SVR预测模型,系统在低干扰的情况下预测精度提高了4.52%,在高干扰时预测精度提高了5.37%。     

Bayesian Quantile Structural Equation Models

Structural equation modeling is a common multivariate technique for the assessment of the interrelationships among latent variables. Structural equation models have been extensively applied to behavioral, medical, and social sciences. Basic structural equation models consist of a measurement equation for characterizing latent variables through multiple observed variables and a mean regression-type structural equation for investigating how explanatory latent variables influence outcomes of interest. However, the conventional structural equation does not provide a comprehensive analysis of the relationship between latent variables. In this article, we introduce the quantile regression method into structural equation models to assess the conditional quantile of the outcome latent variable given the explanatory latent variables and covariates. The estimation is conducted in a Bayesian framework with Markov Chain Monte Carlo algorithm. The posterior inference is performed with the help of asymmetric Laplace distribution. A simulation shows that the proposed method performs satisfactorily. An application to a study of chronic kidney disease is presented.     

国外语篇理解的推理研究述略

推理是语篇中没有明确提到却被激活的信息,或从语篇已知的语义信息中产生新语义信息的过程。对推理的研究一直是语篇阅读理解研究关键。通过从国外语篇推理研究的三个阶段、语篇理解中推理的分类以及语篇理解的推理理论与模型三个方面对国外语篇理解中的推理研究进行总结、回顾和探讨,希望有助于对语篇阅读理解的研究。     

The impact of reading at rapid rates on inference generation

Aims

Speed reading is advertised as a way to increase reading speed without any loss in comprehension. However, research on speed reading has indicated that comprehension suffers as reading speed increases. We were specifically interested in how processes of inference generation were affected by speed reading.

Methods

We examined how reading speed influenced inference generation in typical readers, trained speed readers and participants trained to skim read passages. Passages either strongly or weakly promoted a bridging or predictive inference. After reading, participants performed a lexical decision task on either a nonword, neutral or inference‐related word.

Results

Typical readers responded to strong and weak inference words faster than neutral words. There were no statistical differences in reaction time between inference‐related and neutral words for speed and skim readers.

Conclusions

These findings provide no substantive evidence that the appropriate inferences are generated when reading at rapid speeds. Thus, speed reading may be detrimental to normal integrative comprehension processes.