Diffusion of Lexical Change in Social Media

Computer-mediated communication is driving fundamental changes in the nature of written language. We investigate these changes by statistical analysis of a dataset comprising 107 million Twitter messages (authored by 2.7 million unique user accounts). Using a latent vector autoregressive model to aggregate across thousands of words, we identify high-level patterns in diffusion of linguistic change over the United States. Our model is robust to unpredictable changes in Twitter's sampling rate, and provides a probabilistic characterization of the relationship of macro-scale linguistic influence to a set of demographic and geographic predictors. The results of this analysis offer support for prior arguments that focus on geographical proximity and population size. However, demographic similarity -- especially with regard to race -- plays an even more central role, as cities with similar racial demographics are far more likely to share linguistic influence. Rather than moving towards a single unified "netspeak" dialect, language evolution in computer-mediated communication reproduces existing fault lines in spoken American English.Comment: preprint of PLOS-ONE paper from November 2014; PLoS ONE 9(11) e11311

Use of a Bayesian belief network to predict the impacts of commercializing non-timber forest products on livelihoods

Commercialization of non-timber forest products (NTFPs) has been widely promoted as a means of sustainably developing tropical forest resources, in a way that promotes forest conservation while supporting rural livelihoods. However, in practice, NTFP commercialization has often failed to deliver the expected benefits. Progress in analyzing the causes of such failure has been hindered by the lack of a suitable framework for the analysis of NTFP case studies, and by the lack of predictive theory. We address these needs by developing a probabilistic model based on a livelihood framework, enabling the impact of NTFP commercialization on livelihoods to be predicted. The framework considers five types of capital asset needed to support livelihoods: natural, human, social, physical, and financial. Commercialization of NTFPs is represented in the model as the conversion of one form of capital asset into another, which is influenced by a variety of socio-economic, environmental, and political factors. Impacts on livelihoods are determined by the availability of the five types of assets following commercialization. The model, implemented as a Bayesian Belief Network, was tested using data from participatory research into 19 NTFP case studies undertaken in Mexico and Bolivia. The model provides a novel tool for diagnosing the causes of success and failure in NTFP commercialization, and can be used to explore the potential impacts of policy options and other interventions on livelihoods. The potential value of this approach for the development of NTFP theory is discussed

Accounting for multiple impacts of the Common agricultural policies in rural areas: an analysis using a Bayesian networks approach

In evaluating the potential effects of the reforms of the Common Agricultural Policy, a particularly challenging issue is the representation of the complexity of rural systems either in a static or dynamic framework. In this paper we use Bayesian networks, to the best knowledge of the authors, basically ignored by the literature on rural development. The objective of this paper is to discuss the potential use of Bayesian Networks tools to represent the multiple determinants and impacts of the Common Agricultural Policies in rural areas across Europe. The analysis shows the potential use of BNs in terms of representation of the multiple linkages between different components of rural areas and farming systems, though its use as a simulation tool still requires further improvements.Bayesian Networks (BNs), farm-household, multiple outcomes, Agricultural and Food Policy, Q1, Q18,

Neural network based approximations to posterior densities: a class of flexible sampling methods with applications to reduced rank models

Likelihoods and posteriors of econometric models with strong endogeneity and weakinstruments may exhibit rather non-elliptical contours in the parameter space.This feature also holds for cointegration models when near non-stationarity occursand determining the number of cointegrating relations is a nontrivial issue, and in mixture processes where the modes are relatively far apart. The performance ofMonte Carlo integration methods like importance sampling or Markov ChainMonte Carlo procedures greatly depends in all these cases on the choice of the importance or candidate density. Such a density has to be `close' to the targetdensity in order to yield numerically accurate results with efficient sampling. Neural networks seem to be natural importance or candidate densities, as they havea universal approximation property and are easy to sample from. That is, conditionallyupon the specification of the neural network, sampling can be done either directly orusing a Gibbs sampling technique, possibly using auxiliary variables. A key step in the proposed class of methods is the construction of a neural network that approximatesthe target density accurately. The methods are tested on a set of illustrative modelswhich include a mixture of normal distributions, a Bayesian instrumental variable regression problem with weak instruments and near non-identification, a cointegrationmodel with near non-stationarity and a two-regime growth model for US recessionsand expansions. These examples involve experiments with non-standard, non-ellipticalposterior distributions. The results indicate the feasibility of theneural network approach.Markov chain Monte Carlo;Bayesian inference;neural networks;importance sample

On the Differential Privacy of Bayesian Inference

We study how to communicate findings of Bayesian inference to third parties, while preserving the strong guarantee of differential privacy. Our main contributions are four different algorithms for private Bayesian inference on proba-bilistic graphical models. These include two mechanisms for adding noise to the Bayesian updates, either directly to the posterior parameters, or to their Fourier transform so as to preserve update consistency. We also utilise a recently introduced posterior sampling mechanism, for which we prove bounds for the specific but general case of discrete Bayesian networks; and we introduce a maximum-a-posteriori private mechanism. Our analysis includes utility and privacy bounds, with a novel focus on the influence of graph structure on privacy. Worked examples and experiments with Bayesian na{\"i}ve Bayes and Bayesian linear regression illustrate the application of our mechanisms.Comment: AAAI 2016, Feb 2016, Phoenix, Arizona, United State

Marginal equivalence in v-spherical models

Bayesian Statistics;Models