Refining a Bayesian network using a chain event graph

The search for a useful explanatory model based on a Bayesian Network (BN) now has a long and successful history. However, when the dependence structure between the variables of the problem is asymmetric then this cannot be captured by the BN. The Chain Event Graph (CEG) provides a richer class of models which incorporates these types of dependence structures as well as retaining the property that conclusions can be easily read back to the client. We demonstrate on a real health study how the CEG leads us to promising higher scoring models and further enables us to make more refined conclusions than can be made from the BN. Further we show how these graphs can express causal hypotheses about possible interventions that could be enforced

Cross-National Predictors of Crime: A Meta-Analysis

Cross-national research has increased in the past few decades, resulting in a large body of empirical research. In particular, cross-national studies are often limited in data sources, which restrict variable selection to debatable proxy indicators. This study therefore uses meta-analytic techniques to examine major cross-national predictors of homicide to determine strengths and weaknesses in theory and design. The findings indicate several critical limitations to cross-national research, including biased sample composition, a lack of theoretical clarity in predictor operationalizations, and an overwhelming reliance on cross-sectional design. The predictors that showed the strongest mean effects were Latin American regional dummy variables, income inequality indicators and the Decommodification Index. Conversely, static population indicators, democracy indices, and measures of economic development had the weakest effects on homicide