On the Flexibility of Metropolis–Hastings Acceptance Probabilities in Auxiliary Variable Proposal Generation

Abstract. Use of auxiliary variables for generating proposal variables within a Metropolis–Hastings setting has been suggested in many different settings. This has in particular been of interest for simulation from complex distributions such as multimodal distributions or in transdimensional approaches. For many of these approaches, the acceptance probabilities that are used turn up somewhat magic and different proofs for their validity have been given in each case. In this article, we will present a general framework for construction of acceptance probabilities in auxiliary variable proposal generation. In addition to showing the similarities between many of the proposed algorithms in the literature, the framework also demonstrates that there is a great flexibility in how to construct acceptance probabilities. With this flexibility, alternative acceptance probabilities are suggested. Some numerical experiments are also reported.     

Changing times: Migrants’ social network analysis and the challenges of longitudinal research

Focusing on migrant social networks, this paper draws upon the sociology of time to incorporate complex notions of temporality into the research process. In so doing, we consider firstly, the challenge of going ‘beyond the snapshot’ in data collection to capture dynamism through time. Secondly, we apply the concepts of timescapes to explore ways of addressing the wider context and the interplay between spatiality, temporality and relationality in migration research. We argue that integrating a mixed methods approach to SNA, crucially including visualisation, can provide a useful methodological and analytical framework to understand dynamics.SNA can also be helpful in bridging the personal and structural dimensions in migration research, by providing a meso level of analysis. However, it is also important to connect the investigation of local and transnational networks with an analysis of the broader social, economic and political contexts in which these take shape; in other words, connecting the micro and the meso with the wider macro level. Drawing upon reflections from our migration research studies, we argue that different combinations of quantitative, qualitative and visual methods do not just provide richer sets of data and insights, but can allow us to better connect conceptualisations – and ontologies – of social networks with specific methodological frameworks.     

Generic Processes in Aligning the Multiple Bases of Identity: The Case of Becoming a Ministry Student

Identity theory distinguishes three bases of identity—role, group, and person—but studies have typically focused on one identity at a time. The interrelationship among the multiple bases of identity remains understudied. This study examines the multiple bases of identity individuals engage on their way to becoming ministry students. The results reveal the advantage of examining the multiple bases of identity as subcultural processes, the utility of qualitative research for expanding the empirical scope of identity theory, and the possibility of employing structural symbolic interactionist concepts within a processual symbolic interactionist agenda.     

清代新疆流人诗作的边疆之情

清代新疆流放名人的诗作，在对新疆自然风光、人文风情的赞美中，融汇了诗人浓厚的诗情和传统文化意识。他们情系祖国统一，表达了对边疆美丽风光和各族人民的热爱及自己深厚的爱国热情。     

Causal inferences on the effectiveness of complex social programs: Navigating assumptions,sources of complexity and evaluation design challenges

This paper explores avenues for navigating evaluation design challenges posed by complex social programs (CSPs) and their environments when conducting studies that call for generalizable, causal inferences on the intervention’s effectiveness. A definition is provided of a CSP drawing on examples from different fields, and an evaluation case is analyzed in depth to derive seven (7) major sources of complexity that typify CSPs, threatening assumptions of textbook-recommended experimental designs for performing impact evaluations. Theoretically-supported, alternative methodological strategies are discussed to navigate assumptions and counter the design challenges posed by the complex configurations and ecology of CSPs. Specific recommendations include: sequential refinement of the evaluation design through systems thinking, systems-informed logic modeling; and use of extended term, mixed methods (ETMM) approaches with exploratory and confirmatory phases of the evaluation. In the proposed approach, logic models are refined through direct induction and interactions with stakeholders. To better guide assumption evaluation, question-framing, and selection of appropriate methodological strategies, a multiphase evaluation design is recommended.     

Combining forecasts of electricity consumption in China with time-varying weights updated by a high-order Markov chain model

Electricity consumption forecasting has been always playing a vital role in power system management and planning. Inaccurate prediction may cause wastes of scarce energy resource or electricity shortages. However, forecasting electricity consumption has proven to be a challenging task due to various unstable factors. Especially, China is undergoing a period of economic transition, which highlights this difficulty. This paper proposes a time-varying-weight combining method, i.e. High-order Markov chain based Time-varying Weighted Average (HM-TWA) method to predict the monthly electricity consumption in China. HM-TWA first calculates the in-sample time-varying combining weights by quadratic programming for the individual forecasts. Then it predicts the out-of-sample time-varying adaptive weights through extrapolating these in-sample weights using a high-order Markov chain model. Finally, the combined forecasts can be obtained. In addition, to ensure that the sample data have the same properties as the required forecasts, a reasonable multi-step-ahead forecasting scheme is designed for HM-TWA. The out-of-sample forecasting performance evaluation shows that HM-TWA outperforms the component models and traditional combining methods, and its effectiveness is further verified by comparing it with some other existing models.     

Evaluating Probabilistic Forecasts with Bayesian Signal Detection Models

We propose the use of signal detection theory (SDT) to evaluate the performance of both probabilistic forecasting systems and individual forecasters. The main advantage of SDT is that it provides a principled way to distinguish the response from system diagnosticity, which is defined as the ability to distinguish events that occur from those that do not. There are two challenges in applying SDT to probabilistic forecasts. First, the SDT model must handle judged probabilities rather than the conventional binary decisions. Second, the model must be able to operate in the presence of sparse data generated within the context of human forecasting systems. Our approach is to specify a model of how individual forecasts are generated from underlying representations and use Bayesian inference to estimate the underlying latent parameters. Given our estimate of the underlying representations, features of the classic SDT model, such as the receiver operating characteristic (ROC) curve and the area under the ROC curve (AUC), follow immediately. We show how our approach allows ROC curves and AUCs to be applied to individuals within a group of forecasters, estimated as a function of time, and extended to measure differences in forecastability across different domains. Among the advantages of this method is that it depends only on the ordinal properties of the probabilistic forecasts. We conclude with a brief discussion of how this approach might facilitate decision making.     

A Bayesian approach to non-parametric monotone function estimation

Summary.  The paper proposes two Bayesian approaches to non-parametric monotone function estimation. The first approach uses a hierarchical Bayes framework and a characterization of smooth monotone functions given by Ramsay that allows unconstrained estimation. The second approach uses a Bayesian regression spline model of Smith and Kohn with a mixture distribution of constrained normal distributions as the prior for the regression coefficients to ensure the monotonicity of the resulting function estimate. The small sample properties of the two function estimators across a range of functions are provided via simulation and compared with existing methods. Asymptotic results are also given that show that Bayesian methods provide consistent function estimators for a large class of smooth functions. An example is provided involving economic demand functions that illustrates the application of the constrained regression spline estimator in the context of a multiple-regression model where two functions are constrained to be monotone.     

网络游戏“私服”、“外挂”行为的民事法律责任研究

“私服”、“外挂”行为严重威胁和冲击着网络游戏产业的健康发展。界定了“私服”、“外挂’’行为，指出了“私服”、“外挂”行为的危害性．并在此基础上进一步研究了“私服”、“外挂”行为构成要件内容的特殊性、承担民事责任的方式和损害赔偿数额的确定，最后提出了“私服”、“外挂”的立法建议。这有利于网络游戏产业的健康发展，净化网络文化环境，打击“私服”、“外挂”行为。     

提高体育考试权重扭转学生体质下降趋势

本文以博弈论为工具分析体质健康利益各方，得出“片面追求升学率”是动态博弈的必然结果。在考试招生制度变动不大预期下，建议加大体质健康测评在各级考试招生中的权重，以扭转学生体质下降趋势。