Welcome seminar: Alberto Cassese, Giulia Cereda, Cecilia Viscardi

The seminar will be held on Friday 2nd December 2022, in Aula 205 (ex 32) (DISIA – Viale Morgagni 59). 

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Speaker: GIULIA CEREDA

Title: Comparing different methods for the rare type match problem

Abstract: A classical problem of forensic statistics is that of evaluating a match between a DNA profile found on the crime scene and a suspect’s DNA profile, in the light of the two competing hypotheses (the crime stain has been left by the suspect or by another person).
The evaluation is based on the calculation of the likelihood ratio, but the likelihood of the data under the competing hypotheses is unknown. The “rare type match problem” is the situation in which the matching DNA profile is not in the database of reference, hence it is difficult to have an idea of its frequency in the population. In the last years, I have proposed and analyzed different models and methods (frequentist, Bayesian, parametric and non-parametric) to evaluate the LR for the rare type match case. They are based on quite diverse assumptions and data reduction, and deserve a comparative framework to compare such contributions both theoretically, discussing their rationales, and empirically, by assessing their performances through some validation experiments and appropriate metrics. This is realized by tailoring to the rare type match problem the  ECE (Empirical Cross Entropy) plots, a graphical tool based on information theory that allows to study the accuracy of each method according to their discrimination power and calibration.

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Speaker: CECILIA VISCARDI

Title: Approximate Bayesian computation: methodological developments and novel applications

Abstract: Approximate Bayesian computation (ABC) is a class of simulation-based methods for drawing Bayesian inference when the likelihood function is unavailable or computationally demanding to evaluate. ABC methods dispense with exact likelihood computation as they only require the availability of a simulator model — a computer program which takes parameter values as input, performs stochastic calculations, and returns simulated data.  In the simplest form, ABC algorithms draw parameter proposals from the prior distribution, run the simulator with those values as inputs, and retain proposals such that the simulated data are sufficiently close to the observed data. Despite ABC algorithms having had a tremendous evolution in the last 20 years, most of them still suffer from shortcomings related to i) the waste of computational resources due to the typical rejection step; ii) the inefficient exploration of the parameter space; iii) the computational cost of the simulator. During this talk, I will outline some methodological developments motivated by the above mentioned problems, as well as possible applications in the civil engineering, epidemiological and forensic fields.

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Speaker: ALBERTO CASSESE

Title: Long story short: 11 years of (my) research summarized in 30 minutes

Abstract: In this welcome seminar I will show a general overview of the research projects I worked on (and I am still working on). In the first half, I will focus on my work in the field of Bayesian analysis, specifically on methods for the analysis of high dimensional data and Bayesian non-parametrics. In the second half I will focus on more recent work on studying two-way interaction by means of biclustering and optimization of research study designs in reliability and agreement studies.