Active citizenship: participatory patterns of European youth

Purpose: Treating Active Citizenship as a sum of behavioural indicators requires certain prerequisites that can be difficult to meet in practice (e.g. structural validity and measurement invariance). We explore a different approach, in which we treat Active Citizenship as a categorical, rather than a linear, construct. Design: Based on longitudinal data from eight European countries, we discovered the patterns’ structure based on the first-year data and then replicated the analysis on the second-year sample to confirm it. Next, we explored the change between the years and its’ trajectories. We compared countries profiles and their change. Finally, we used multinomial logistic regression to explore the most common trajectories. Findings: We describe six patterns: fighter, activist, volunteer, backer, online and indifferent. The pattern structure is replicable and 41.8% of respondents preserve their pattern. For those respondents who changed their pattern, we identified political interest, religiosity, gender and age as the main factors behind this change. Research implications: The study contributes to the understanding of youth Active Citizenship and the factors that support and promote it

Thermodynamic assessment of probability distribution divergencies and Bayesian model comparison

Within path sampling framework, we show that probability distribution divergences, such as the Chernoff information, can be estimated via thermodynamic integration. The Boltzmann-Gibbs distribution pertaining to different Hamiltonians is implemented to derive tempered transitions along the path, linking the distributions of interest at the endpoints. Under this perspective, a geometric approach is feasible, which prompts intuition and facilitates tuning the error sources. Additionally, there are direct applications in Bayesian model evaluation. Existing marginal likelihood and Bayes factor estimators are reviewed here along with their stepping-stone sampling analogues. New estimators are presented and the use of compound paths is introduced

Computational statistics using the Bayesian Inference Engine

This paper introduces the Bayesian Inference Engine (BIE), a general parallel, optimised software package for parameter inference and model selection. This package is motivated by the analysis needs of modern astronomical surveys and the need to organise and reuse expensive derived data. The BIE is the first platform for computational statistics designed explicitly to enable Bayesian update and model comparison for astronomical problems. Bayesian update is based on the representation of high-dimensional posterior distributions using metric-ball-tree based kernel density estimation. Among its algorithmic offerings, the BIE emphasises hybrid tempered MCMC schemes that robustly sample multimodal posterior distributions in high-dimensional parameter spaces. Moreover, the BIE is implements a full persistence or serialisation system that stores the full byte-level image of the running inference and previously characterised posterior distributions for later use. Two new algorithms to compute the marginal likelihood from the posterior distribution, developed for and implemented in the BIE, enable model comparison for complex models and data sets. Finally, the BIE was designed to be a collaborative platform for applying Bayesian methodology to astronomy. It includes an extensible object-oriented and easily extended framework that implements every aspect of the Bayesian inference. By providing a variety of statistical algorithms for all phases of the inference problem, a scientist may explore a variety of approaches with a single model and data implementation. Additional technical details and download details are available from http://www.astro.umass.edu/bie. The BIE is distributed under the GNU GPL.Comment: Resubmitted version. Additional technical details and download details are available from http://www.astro.umass.edu/bie. The BIE is distributed under the GNU GP