Ricerc@Sapienza

The goal of statistical matching, at amacrolevel, is the estimation of the joint distribution of variables separately observed in independent samples. The lack of joint informationon the variables of interest leads to uncertainty about the data generating model. In this paper we propose the use of graphical models to deal withthe statistical matching uncertainty for multivariate categorical variables.

This paper proposes to learn an approximate Bayesian Network (BN) model from Monte-Carlo simulations of an infrastructure system exposed to seismic hazard. Exploiting preliminary physical simulations has the twofold benefit of building a drastically simplified BN and of predicting complex system performance metrics. While the approximate BN cannot yield exact probabilities for predictive analyses, its use in backward analyses based on evidenced variables yields promising results as a decision support tool for post-earthquake rapid response.

The paper investigates if and how the topology of the peer assessment network can affect the performance of the Bayesian model adopted in Ope

Questions with answers are rarely used as e-learning assessment tools because of the resulting high workload for the teacher/tutor that should grade them, which can be mitigated by doing peer-assessment. In OpenAnswer we modeled peer-assessment as a Bayesian network connecting the sub-networks representing any participating student to the corresponding answers of her graded peers.

The salutogenicity of urban environments is significantly affected by their ergonomics, i.e., by the quality of the interactions between citizens and the elements of the built environment. Measuring and modelling urban ergonomics is thus a key issue to provide urban policy makers with planning solutions to increase the well-being, usability and safety of the urban environment. However, this is a difficult task due to the complexity of the interrelations between the urban environment and human activities.