Multivariate normal integrals and contingency tables with ordered categories

Ak-dimensional multivariate normal distribution is made discrete by partitioning thek-dimensional Euclidean space with rectangular grids. The collection of probability integrals over the partitioned cubes is ak-dimensional contingency table with ordered categories. It is shown that loglinear model with main effects plus two-way interactions provides an accurate approximation for thek-dimensional table. The complete multivariate normal integral table is computed via the iterative proportional fitting algorithm from bivariate normal integral tables. This approach imposes no restriction on the correlation matrix. Comparisons with other numerical integration algorithms are reported. The approximation suggests association models for discretized multivariate normal distributions and contingency tables with ordered categories.The contingency-table approach occurred to me while I was collaborating with Paul Holland of the Educational Testing Service in 1985 on bivariate dependence functions. Holland maintains a belief that the continuous can learn from the discrete. This work is a reassertion of his claim.This research was sponsored by the National Science Council, Republic of China.     

汉语句子格角色分配的一种神经网络方法

提出了一个基于分布式表征的计算模型,通过并行分布加工方式完成六类汉语句子的格角色分配任务。模型是一个四层的前传网络,包括输入层（词的分布式表征层）,两个隐层,输出层（格角色层）;其中第一隐层的一部分反馈到输入层。模型采用误差反传算法,通过提供学习样本和目标输出,不断调整三个权值矩阵,使得网络稳定时能得到正确的结果。经过训练后的网络具有一定的稳定性和鲁棒性。还对这种方法与传统的符号处理方法作了比较和分析。     

Marginal maximum likelihood estimation of item parameters: Application of an EM algorithm

Maximum likelihood estimation of item parameters in the marginal distribution, integrating over the distribution of ability, becomes practical when computing procedures based on an EM algorithm are used. By characterizing the ability distribution empirically, arbitrary assumptions about its form are avoided. The Em procedure is shown to apply to general item-response models lacking simple sufficient statistics for ability. This includes models with more than one latent dimension.Supported in part by NSF grant BNS 7912417 to the University of Chicago and by SSRC (UK) grant HR6132 to the University of Lancaster.We are indebted to Mark Reiser and Robert Gibbons for computer programming. David Thissen clarified a number of points in an earlier draft.     

Preference for human or algorithmic forecasting advice does not predict if and how it is used

Past research has found that people treat advice differently depending on its source. In many cases, people seem to prefer human advice to algorithms, but in others, there is a reversal, and people seem to prefer algorithmic advice. Across two studies, we examine the persuasiveness of, and judges' preferences for, advice from different sources when forecasting geopolitical events. We find that judges report domain-specific preferences, preferring human advice in the domain of politics and algorithmic advice in the domain of economics. In Study 2, participants report a preference for hybrid advice, that combines human and algorithmic sources, to either one on it's own regardless of domain. More importantly, we find that these preferences did not affect persuasiveness of advice from these different sources, regardless of domain. Judges were primarily sensitive to quantitative features pertaining to the similarity between their initial beliefs and the advice they were offered, such as the distance between them and the relative advisor confidence, when deciding whether to revise their initial beliefs in light of advice, rather than the source that generated the advice.     

项目反应理论中潜在心理特质“填补”的参数估计方法及其演变

在心理与教育测量中, 项目反应理论(Item Response Theory, IRT)模型的参数估计方法是理论研究与实践应用的基本工具。最近, 由于IRT模型的不断扩展与EM (expectation-maximization)算法自身的固有问题, 参数估计方法的改进与发展显得尤为重要。这里介绍了IRT模型中边际极大似然估计的发展, 提出了它的阶段性特征, 即联合极大似然估计阶段、确定性潜在心理特质“填补”阶段、随机潜在心理特质“填补”阶段, 重点阐述了它的潜在心理特质“填补” (data augmentation)思想。EM算法与Metropolis-Hastings Robbins-Monro (MH-RM)算法作为不同的潜在心理特质“填补”方法, 都是边际极大似然估计的思想跨越。目前, 潜在心理特质“填补”的参数估计方法仍在不断发展与完善。     

Dynamic analysis of multivariate panel data with non-linear transformations

Many models for multivariate data analysis can be seen as special cases of the linear dynamic or state space model. Contrary to the classical approach to linear dynamic systems analysis, in which high-dimensional exact solutions are sought, the model presented here is developed from a social science framework where low-dimensional approximate solutions are preferred. Borrowing concepts from the theory on mixture distributions, the linear dynamic model can be viewed as a multi-layered regression model, in which the output variables are imprecise manifestations of an unobserved continuous process. An additional layer of mixing makes it possible to incorporate non-normal as well as ordinal variables.Using the EM-algorithm, we find estimates of the unknown model parameters, simultaneously providing stability estimates. The model is very general and cannot be well estimated by other estimation methods. We illustrate the applicability of the obtained procedure through an example with generated data.     

A computational model of sequential movement learning with a signal mimicking dopaminergic neuron activities

We present a computational model of approach learning in a simulated maze environment. Our maze environment and training method mimics those used in the experimental literature. We show that our model learns the correct sequence of six decisions that lead to the location of positive reinforcement and in a manner consistent with experimental observations. Our model exhibits many properties that are characteristic of animal learning in maze environments including delay conditioning, secondary conditioning, and backward chaining. Finally, we map our model to the basal ganglia and show that a signal in our model that is responsible for learning has the same temporal properties as dopamine, the neurotransmitter believed to play an important part in learning decision sequences.     

基于HO-DINA模型的多级评分认知诊断模型的开发

本文对具有较好发展前景的HO-DINA模型进行拓展,将仅适用于0-1评分题型的HO-DINA模型拓广至可用于多级评分题型,采用MCMC算法实现了对模型参数的估计,并对新模型性能进行了研究。研究发现: （1）本文拓展的多级评分HO-DINA模型参数估计精度较高且诊断正确率较高。（2）多级评分的HO-DINA模型诊断的属性个数越多,属性参数（ 和 ）和s参数估计的精度越差、属性诊断的正确率（MMR和PRM）越低,但能力参数（ ）和g参数的估计精度反而越高。（3）在当前条件下,若想保证属性模式判准率在80%以上,建议诊断的属性个数不宜超过7个。     

Newton Algorithms for Analytic Rotation: an Implicit Function Approach

In this paper implicit function-based parameterizations for orthogonal and oblique rotation matrices are proposed. The parameterizations are used to construct Newton algorithms for minimizing differentiable rotation criteria applied to m factors and p variables. The speed of the new algorithms is compared to that of existing algorithms and to that of Newton algorithms based on alternative parameterizations. Several rotation criteria were examined and the algorithms were evaluated over a range of values for m. Initial guesses for Newton algorithms were improved by subconvergence iterations of the gradient projection algorithm. Simulation results suggest that no one algorithm is fastest for minimizing all criteria for all values of m. Among competing algorithms, the gradient projection algorithm alone was faster than the implicit function algorithm for minimizing a quartic criterion over oblique rotation matrices when m is large. In all other conditions, however, the implicit function algorithms were competitive with or faster than the fastest existing algorithms. The new algorithms showed the greatest advantage over other algorithms when minimizing a nonquartic component loss criterion.     

Human actions recognition in video scenes from multiple camera viewpoints

Internet of Video Things (IoVT) has been proposed and studied as a scenario where video cameras are ubiquitous and continuously acquiring data from their surroundings. In order to enable handling of a large amount of data generated in IoVT architectures, robust autonomous video processing must be performed. An important application is the recognition of different actions performed by humans in the context of security. This research evolves a previously published work, by reducing the input dimensionality to the recognition system, making it more robust to variations in the position of the body in each video frame, and by using a Multilayer Perceptron Artificial Neural Network whose hyperparameters are here optimized by a Genetic Algorithm. Significant improvements in the recognition rate have been obtained, despite the use of a more straightforward pre-processing phase and the increase in the number of viewpoints from the video cameras.