A bayesian spatial individual effects probit model of the 2010 UK general election

The Conservative Party emerged from the 2010 United Kingdom General Election as the largest single party, but their support was not geographically uniform. In this paper, we estimate a hierarchical Bayesian spatial probit model that tests for the presence of regional voting effects. This model allows for the estimation of individual region-specic effects on the probability of Conservative Party success, incorporating information on the spatial relationships between the regions of the mainland United Kingdom. After controlling for a range of important covariates, we find that these spatial relationships are significant and that our individual region-specic effects estimates provide additional evidence of North-South variations in Conservative Party support.

Bayesian forecasting with highly correlated predictors

This paper considers Bayesian variable selection in regressions with a large number of possibly highly correlated macroeconomic predictors. I show that by acknowledging the correlation structure in the predictors can improve forecasts over existing popular Bayesian variable selection algorithms.

Anticipation, Learning and Welfare: the Case of Distortionary Taxation

We study the impact of anticipated fiscal policy changes in a Ramsey economy where agents form long-horizon expectations using adaptive learning. We extend the existing framework by introducing distortionary taxes as well as elastic labour supply, which makes agents. decisions non-predetermined but more realistic. We detect that the dynamic responses to anticipated tax changes under learning have oscillatory behaviour that can be interpreted as self-fulfilling waves of optimism and pessimism emerging from systematic forecast errors. Moreover, we demonstrate that these waves can have important implications for the welfare consequences of .scal reforms. (JEL: E32, E62, D84)

Unilateral Emissions Mitigation, Spillovers, and Global Learning

What's the role of unilateral measures in global climate change mitigation in a post-Durban, post 2012 global policy regime? We argue that under conditions of preference heterogeneity, unilateral emissions mitigation at a subnational level may exist even when a nation is unwilling to commit to emission cuts. As the fraction of individuals unilaterally cutting emissions in a global strongly connected network of countries evolves over time, learning the costs of cutting emissions can result in the adoption of such activities globally and we establish that this will indeed happen under certain assumptions. We analyze the features of a policy proposal that could accelerate convergence to a low carbon world in the presence of global learning.

The Effects of Experience on Preference Uncertainty: Theory and Empirics for Public and Quasi-Public Environmental Goods

This paper develop and estimates a model of demand estimation for environmental public goods which allows for consumers to learn about their preferences through consumption experiences. We develop a theoretical model of Bayesian updating, perform comparative statics over the model, and show how the theoretical model can be consistently incorporated into a reduced form econometric model. We then estimate the model using data collected for two environmental goods. We find that the predictions of the theoretical exercise that additional experience makes consumers more certain over their preferences in both mean and variance are supported in each case.

Large Bayesian VARMAs

Vector Autoregressive Moving Average (VARMA) models have many theoretical properties which should make them popular among empirical macroeconomists. However, they are rarely used in practice due to over-parameterization concerns, difficulties in ensuring identification and computational challenges. With the growing interest in multivariate time series models of high dimension, these problems with VARMAs become even more acute, accounting for the dominance of VARs in this field. In this paper, we develop a Bayesian approach for inference in VARMAs which surmounts these problems. It jointly ensures identification and parsimony in the context of an efficient Markov chain Monte Carlo (MCMC) algorithm. We use this approach in a macroeconomic application involving up to twelve dependent variables. We find our algorithm to work successfully and provide insights beyond those provided by VARs.

Benzodiazepine receptor ligand influences on learning: an endogenous modulatory mechanism mediated by benzodiazepines possibly of alimentary origin

In rats pre-but not post-training ip administration of either flumazenil, a central benzodiazepine (BSD) receptor antagonist, or of n-butyl-B-carboline-carboxylate (BCCB), an inverse agonist, enhanced retention of inhibitory avoidance learning. Flumazenil vlocked the enhancing effect of BCCB, and the inhibitory effect of the BZD agonists clonazepam and diazepam also given pre-training. Post-training administration of these drugs had no effects. The peripheral BZD receptor agonist/chloride channel blocker Ro5-4864 had no effect on the inhibitory avoidance task when given ip prior to training, buth it caused enhancement when given immediately post-training either ip or icv. This effect was blocked by PK11195, a competitive antagonist of Ro5-4864. These results suggest that ther is an endogenous mechanism mediated by BZD agonists, which is sensitive to inverse agonists and that normally down-regulates the formation of memories through a mechanism involving GABA-A receptors and the corresponding chloride channels. The most likely agonists for the endogenous mechanism suggested are the diazepam-like BZDs found in brain whose origin is possibly alimentary. Levels of these BZDs in the cortex were found to sharply decrease after inhibitory acoidance training or mere exposure to the training apparatus.

Learning to live together: an exploration and analysis of managing cultural diversity in ten early childhood development centres in South Africa

This study explores how South African Early Childhood Development (ECD) Practitioners and families meet the needs of the increasing number of children from diverse cultural backgrounds in their care. Research participants were identified through ten ECD centres located in two urban communities in the Eastern and Western Cape Provinces of South Africa. The values and attitudes held by Practitioners and families vis-à-vis cultural diversity was investigated, along with the knowledge and strategies they employ to manage cultural diversity in ECD programmes. The intercultural education model provides the necessary tools to address the challenges identified.

Learning-induced plasticity in human audition: Objects, time, and space.

The human auditory system is comprised of specialized but interacting anatomic and functional pathways encoding object, spatial, and temporal information. We review how learning-induced plasticity manifests along these pathways and to what extent there are common mechanisms subserving such plasticity. A first series of experiments establishes a temporal hierarchy along which sounds of objects are discriminated along basic to fine-grained categorical boundaries and learned representations. A widespread network of temporal and (pre)frontal brain regions contributes to object discrimination via recursive processing. Learning-induced plasticity typically manifested as repetition suppression within a common set of brain regions. A second series considered how the temporal sequence of sound sources is represented. We show that lateralized responsiveness during the initial encoding phase of pairs of auditory spatial stimuli is critical for their accurate ordered perception. Finally, we consider how spatial representations are formed and modified through training-induced learning. A population-based model of spatial processing is supported wherein temporal and parietal structures interact in the encoding of relative and absolute spatial information over the initial ∼300ms post-stimulus onset. Collectively, these data provide insights into the functional organization of human audition and open directions for new developments in targeted diagnostic and neurorehabilitation strategies.

Bayesian Networks for Probabilistic Inference and Decision Analysis in Forensic Science

Continuing developments in science and technology mean that the amounts of information forensic scientists are able to provide for criminal investigations is ever increasing. The commensurate increase in complexity creates difficulties for scientists and lawyers with regard to evaluation and interpretation, notably with respect to issues of inference and decision. Probability theory, implemented through graphical methods, and specifically Bayesian networks, provides powerful methods to deal with this complexity. Extensions of these methods to elements of decision theory provide further support and assistance to the judicial system. Bayesian Networks for Probabilistic Inference and Decision Analysis in Forensic Science provides a unique and comprehensive introduction to the use of Bayesian decision networks for the evaluation and interpretation of scientific findings in forensic science, and for the support of decision-makers in their scientific and legal tasks. Includes self-contained introductions to probability and decision theory. Develops the characteristics of Bayesian networks, object-oriented Bayesian networks and their extension to decision models. Features implementation of the methodology with reference to commercial and academically available software. Presents standard networks and their extensions that can be easily implemented and that can assist in the reader's own analysis of real cases. Provides a technique for structuring problems and organizing data based on methods and principles of scientific reasoning. Contains a method for the construction of coherent and defensible arguments for the analysis and evaluation of scientific findings and for decisions based on them. Is written in a lucid style, suitable for forensic scientists and lawyers with minimal mathematical background. Includes a foreword by Ian Evett. The clear and accessible style of this second edition makes this book ideal for all forensic scientists, applied statisticians and graduate students wishing to evaluate forensic findings from the perspective of probability and decision analysis. It will also appeal to lawyers and other scientists and professionals interested in the evaluation and interpretation of forensic findings, including decision making based on scientific information.

A Survey of Active Learning Algorithms for Supervised Remote Sensing Image Classification

Defining an efficient training set is one of the most delicate phases for the success of remote sensing image classification routines. The complexity of the problem, the limited temporal and financial resources, as well as the high intraclass variance can make an algorithm fail if it is trained with a suboptimal dataset. Active learning aims at building efficient training sets by iteratively improving the model performance through sampling. A user-defined heuristic ranks the unlabeled pixels according to a function of the uncertainty of their class membership and then the user is asked to provide labels for the most uncertain pixels. This paper reviews and tests the main families of active learning algorithms: committee, large margin, and posterior probability-based. For each of them, the most recent advances in the remote sensing community are discussed and some heuristics are detailed and tested. Several challenging remote sensing scenarios are considered, including very high spatial resolution and hyperspectral image classification. Finally, guidelines for choosing the good architecture are provided for new and/or unexperienced user.

Desarrollo de un sistema de e-Learning para Nintendo DS

El proyecto trata de crear un software que dinámicamente nos proporcione exámenes o pruebas dependiendo de nuestro nivel de conocimientos actual. Estos exámenes se cargarán a través de un fichero XML configurable, lo que nos permitirá poner a prueba nuestros conocimientos en el tema que deseemos. El software se desarrollará en Nintendo DS, para aprovechar las prestaciones que nos ofrece de serie: doble pantalla, pantalla táctil, portabilidad.

Interoperability issues between learning object repositories and metadata harvesters

In this paper we describe an open learning object repository on Statistics based on DSpace which contains true learning objects, that is, exercises, equations, data sets, etc. This repository is part of a large project intended to promote the use of learning object repositories as part of the learning process in virtual learning environments. This involves the creation of a new user interface that provides users with additional services such as resource rating, commenting and so. Both aspects make traditional metadata schemes such as Dublin Core to be inadequate, as there are resources with no title or author, for instance, as those fields are not used by learners to browse and search for learning resources in the repository. Therefore, exporting OAI-PMH compliant records using OAI-DC is not possible, thus limiting the visibility of the learning objects in the repository outside the institution. We propose an architecture based on ontologies and the use of extended metadata records for both storing and refactoring such descriptions.