Transnational urbanism in Port of Spain: returning middle-class urban elites

Port of Spain, Trinidad offers an ideal context in which to analyze pre-retirement return migration to a Global South urban realm, expanding transnational urban research beyond the conventional focus on Global North metropolitan destinations. In this article, we draw on the transnational narratives of a selected sample of relatively youthful Trinidadians, who have spent many years abroad acquiring education and professional experience, but who have then decided to return in mid-career to the capital region of the island nation of their birth, or of their parent(s). Theoretically, we position these returning professionals as members of a "middling" transnational urban class whose return is at least partly motivated by a desire to "make a difference." Our results contribute to a growing literature that documents the role of transnational middle-class urban elites returning elsewhere in the Carribbean: "middling" transnational urbanism is reshaping key facets of urbanization in the Global South.

Bayesian parameter estimation for latent Markov random fields and social networks

Undirected graphical models are widely used in statistics, physics and machine vision. However Bayesian parameter estimation for undirected models is extremely challenging, since evaluation of the posterior typically involves the calculation of an intractable normalising constant. This problem has received much attention, but very little of this has focussed on the important practical case where the data consists of noisy or incomplete observations of the underlying hidden structure. This paper specifically addresses this problem, comparing two alternative methodologies. In the first of these approaches particle Markov chain Monte Carlo (Andrieu et al., 2010) is used to efficiently explore the parameter space, combined with the exchange algorithm (Murray et al., 2006) for avoiding the calculation of the intractable normalising constant (a proof showing that this combination targets the correct distribution in found in a supplementary appendix online). This approach is compared with approximate Bayesian computation (Pritchard et al., 1999). Applications to estimating the parameters of Ising models and exponential random graphs from noisy data are presented. Each algorithm used in the paper targets an approximation to the true posterior due to the use of MCMC to simulate from the latent graphical model, in lieu of being able to do this exactly in general. The supplementary appendix also describes the nature of the resulting approximation.

A neurofuzzy classifier for two class problems

A neurofuzzy classifier identification algorithm is introduced for two class problems. The initial fuzzy base construction is based on fuzzy clustering utilizing a Gaussian mixture model (GMM) and the analysis of covariance (ANOVA) decomposition. The expectation maximization (EM) algorithm is applied to determine the parameters of the fuzzy membership functions. Then neurofuzzy model is identified via the supervised subspace orthogonal least square (OLS) algorithm. Finally a logistic regression model is applied to produce the class probability. The effectiveness of the proposed neurofuzzy classifier has been demonstrated using a real data set.