Influence diagrams are probabilistic graphical models used to represent and solve sequential decision problems under uncertainty. Sharp numerical values are required to quantify probabilities and utilities. This might be an issue with real models, whose parameters are typically obtained from expert judgements or partially reliable data. We consider an interval-valued quantification of the param...

MOOCs pedagogical strategies assume that students construct their own knowledge and collaborate with their mates. Largescale learners’ interaction figures hinder both proper interpretation of learners’ needs and prompt remediation actions. To this we describe a preliminary study of a two-step collaboration analysis, which consists of inferring domain-independent indicators on students’ relation...

Severe head injury management in the intensive care unit is extremely challenging due to the complex domain, the uncertain intervention efficacies, and the time-critical setting. We adopt a decision analytic approach to automate the management process. We document our experience in building a simplified influence diagram that involves about 3000 numerical parameters. We identify the inherent pr...

Whilst influence functions for linear discriminant analysis (LDA) have been found a single when dealing with two groups, until now these not derived in the setting of general number groups. In this paper we explore relationship between Sliced Inverse Regression (SIR) and LDA, exploit to develop LDA from those already SIR. These can be used understand robustness properties also detect influentia...

In this paper we develop combinatorial methods for the evaluation of yield and operational reliability of fault-tolerant systems-on-chip. The method for yield computation assumes that defects are produced according to a model in which defects are lethal and affect given components of the system following a distribution common to all defects; the method for the computation of operational reliabi...

This paper explores the role of Directed Acyclic Graphs (DAGs) as a representation of condition­ al independence relationships. We show that DAGs offer polynomially sound and complete inference mechanisms for inferring conditional independence relationships from a given causal set of such relation­ ships. As a consequence, d-separation, a graphical criterion for identifying independencies in a ...