Importance sampling type estimators based on approximate marginal Markov chain Monte Carlo

We consider importance sampling (IS) type weighted estimators based on Markov chain Monte Carlo (MCMC) targeting an approximate marginal of the target distribution. In the context of Bayesian latent variable models, the MCMC typically operates on the hyperparameters, and the subsequent weighting may be based on IS or sequential Monte Carlo (SMC), but allows for multilevel techniques as well. The IS approach provides a natural alternative to delayed acceptance (DA) pseudo-marginal/particle MCMC, and has many advantages over DA, including a straightforward parallelization and additional flexibility in MCMC implementation. We detail minimal conditions which ensure strong consistency of the suggested estimators, and provide central limit theorems with expressions for asymptotic variances. We demonstrate how our method can make use of SMC in the state space models context, using Laplace approximations and time-discretized diffusions. Our experimental results are promising and show that the IS-type approach can provide substantial gains relative to an analogous DA scheme, and is often competitive even without parallelization.     

“阿包”:已知19世纪宁波首位圣公会教徒

原事裁缝业的“阿包”在1846年成为了19世纪宁波首位圣公会教徒。“阿包”信仰笃定,主动向身边的底层百姓布道,成果得到了传教士的肯定。“阿包”个性外向,但有时也脾性不定,这点深深地左右了他与传教士间的人际往来。1858年秋日,他与原本信奉罗马方济各会的中国医生“丁先生”展开了多次教义舌战,最终“阿包”打动了“丁先生”,推动其转入圣公会。“阿包”能以低微社会身份取得较大传教成果的原因主要有三:首先,他有着独特的身世资源,可以与底层民众心心相印;其次,他具备良好个人魅力,能动人以情;复次,他与传教士间有职业、宗教双重纽带绑定。在近代中西交流史上,“阿包”案例不仅构成检视外来文化“在地化”的良好样本,——近代外来文化中国化的历程从来不是精英“独创”,。     

P2P网络借贷机构的系统重要性研究

互联网金融发展得如火如荼,其蕴藏的风险不容忽视。由于P2P网络借贷是互联网金融的一项重要业态,且其风险溢出效应较大,研究其风险监管有着非常重要的意义。通过借鉴巴塞尔委员会对系统重要性银行识别的指标体系,基于指标法的思想联系我国互联网金融发展现状以及P2P网络借贷机构的特征对指标体系进行了合理改进。对国内比较重要的P2P网络借贷机构进行了系统重要性排名,并且和以前的研究成果进行了比较。对于推动互联网金融分类适度的监管工作有着一定的意义。     

论《吕氏春秋》农学思想与《周易》及《老子》

《吕氏春秋》倡导重农的政策思想主要是对《老子》“圣人之治”理论的继承和发展，体现在：“朴”的政治哲学，“不尚贤”的政治主张和老子“小国寡民”的政治情怀。《老子》与《吕氏春秋》之间有两条主轴贯穿始终：一是以老子政治哲学为主的道家“无为”思想，《吕氏春秋》将其浸透在重农政策中；二是《周易》、《老子》思维方式在《吕氏春秋》中的体现，具体表现在《吕氏春秋》的言说方式和言说内容具有正反相合、注重“天地人”三者融通与和谐的整体性思维特点。而《周易》、《老子》的流传与演变，为《吕氏春秋》的思想内容、思维方式和言说特点提供了方便之门。     

学习迁移的系统理论:PPEE理论模型的建构和意义

学习是人类社会永恒的主题。为了更好地利用过去的经验，增强学习效果，个人和组织进行学习迁移具有重要意义。学习迁移是指将在以往通过学习或解决问题所获得的经验，用于解决之后所遇到的问题。本文力图建立系统的学习迁移理论，研究人、问题、环境的因素对于学习迁移的影响。本文采用理论推导的研究方法，在前人研究的基础上，提出相应的理论模型。本文运用系统的观点，构建了学习迁移的PPEE理论模型，认为人的因素、问题的因素、人和问题的交互作用、以及环境的调节作用，会共同影响学习迁移的效果。本理论主要从五个方面论述学习迁移的机制。首先，将人的因素按照不同层面划分为后端基本能力、中端心理过程、前端知识结构，讨论了其各自对学习迁移效果的直接影响。其次，将问题的因素划分为形象相似性和抽象相似性，并根据这两个维度构建了四种问题间关系类型，并探究了其与学习迁移效果之间的关系。第三，同时考虑人的因素和问题的因素，研究了二者之间的互动对于学习迁移效果的影响。第四，将环境因素划分为时间紧迫性和空间重要性两个维度，探讨了人和环境，以及问题和环境之间的互动对学习迁移效果的影响。第五，基于系统整体的方面，研究人、问题、环境三者之间的互动对学习迁移效果的交互影响。本文还认为，该模型不仅适用于个人，也同样适用于团队、组织、地区、甚至国家和社会等各个层面，影响各个层面主体的学习迁移。本文最后总结了研究的理论和实践贡献，并提出了未来研究课题。本文对丰富学习理论、特别是学习迁移理论，以及推动教育和管理实践的发展进步具有重要的意义。     

Robust inference for estimating equations with nonignorably missing data based on SIR algorithm

Nonresponse is a very common phenomenon in survey sampling. Nonignorable nonresponse – that is, a response mechanism that depends on the values of the variable having nonresponse – is the most difficult type of nonresponse to handle. This article develops a robust estimation approach to estimating equations (EEs) by incorporating the modelling of nonignorably missing data, the generalized method of moments (GMM) method and the imputation of EEs via the observed data rather than the imputed missing values when some responses are subject to nonignorably missingness. Based on a particular semiparametric logistic model for nonignorable missing response, this paper proposes the modified EEs to calculate the conditional expectation under nonignorably missing data. We can apply the GMM to infer the parameters. The advantage of our method is that it replaces the non-parametric kernel-smoothing with a parametric sampling importance resampling (SIR) procedure to avoid nonparametric kernel-smoothing problems with high dimensional covariates. The proposed method is shown to be more robust than some current approaches by the simulations.     

我国图书馆员的专业教育与终身学习

首先阐述了专业教育和终身学习的涵义、目的、作用及二者的关系;进而从图书馆的行业特性、我国图书馆的人员现状和图书馆可持续发展的内在要求三方面论述了我国图书馆员进行专业教育和终身学习的重要性和迫切性;最后指出了新世纪图书馆员加强专业教育和终身学习的措施。     

Changing perceived importance of religion in mainland China, 1990–2012: An age-period- cohort analysis

The comprehensive social reform and relaxation of religious regulation in mainland China have encouraged scholars to propose a revival thesis of religion, predicting a rising prevalence of religious adherence in the Reform Era. This study extends the revival thesis by focusing attention on people's subjective religiosity, and investigates age, period, and cohort effects on the transition in perceived importance of religion from 1990 to 2012. Capitalizing on the repeated cross-sectional data of the China sample in the World Values Survey, this study shows that (1) The senior population, relative to the younger counterpart, attaches greater importance to religion. (2) The net period effect suggests that Chinese citizens' perceived importance of religion follows an upward trend by the early 2000s, but no significant growth is detected henceforward. (3) The cohorts who experienced the anti-religion Mao's Era in their adolescent life course stage have an evidently lower probability of viewing religion to be important, in relation to the cohorts of the Reform Era. Theoretical implications of the empirical age-period-cohort patterns for the religious economies theory and change of Chinese religious landscape are discussed.     

Adaptive multiple importance sampling for Gaussian processes

In applications of Gaussian processes (GPs) where quantification of uncertainty is a strict requirement, it is necessary to accurately characterize the posterior distribution over Gaussian process covariance parameters. This is normally done by means of standard Markov chain Monte Carlo (MCMC) algorithms, which require repeated expensive calculations involving the marginal likelihood. Motivated by the desire to avoid the inefficiencies of MCMC algorithms rejecting a considerable amount of expensive proposals, this paper develops an alternative inference framework based on adaptive multiple importance sampling (AMIS). In particular, this paper studies the application of AMIS for GPs in the case of a Gaussian likelihood, and proposes a novel pseudo-marginal-based AMIS algorithm for non-Gaussian likelihoods, where the marginal likelihood is unbiasedly estimated. The results suggest that the proposed framework outperforms MCMC-based inference of covariance parameters in a wide range of scenarios.