Welcome seminar: Alberto Cassese, Giulia Cereda, Cecilia Viscardi<\/p>\n
The seminar will be held on Friday 2nd December 2022<\/strong>, in Aula 205 (ex 32) (DISIA \u2013 Viale Morgagni 59).\u00a0<\/span><\/p>\n ——————–<\/p>\n Speaker: GIULIA CEREDA<\/span><\/p>\n Title<\/b>: Comparing different methods for the rare type match problem<\/p>\n Abstract<\/b>:\u00a0A classical problem of forensic statistics is that of evaluating a match between a DNA profile found on the crime scene and a\u00a0suspect\u2019s DNA profile, in the light of the two competing hypotheses (the crime stain has been left by the suspect or by another person). ******* Title<\/b>: Approximate Bayesian computation: methodological developments and novel applications<\/p>\n Abstract<\/b>:\u00a0Approximate Bayesian computation (ABC) is a class of simulation-based methods for drawing Bayesian inference when the\u00a0likelihood function is unavailable or computationally demanding to evaluate. ABC methods dispense with exact likelihood computation as\u00a0they only require the availability of a simulator model — a computer program which takes parameter values as input, performs\u00a0stochastic calculations, and returns simulated data. \u00a0In the simplest form, ABC algorithms draw parameter proposals from the prior\u00a0distribution, run the simulator with those values as inputs, and retain proposals such that the simulated data are sufficiently close to the\u00a0observed data. Despite ABC algorithms having had a tremendous evolution in the last 20 years, most of them still suffer from\u00a0shortcomings related to i) the waste of computational resources due to the typical rejection step; ii) the inefficient exploration of the\u00a0parameter space; iii) the computational cost of the simulator. During this talk, I will outline some methodological developments\u00a0motivated by the above mentioned problems, as well as possible applications in the civil engineering, epidemiological and forensic\u00a0fields.<\/p>\n ******* Title<\/b>: Long story short: 11 years of (my) research summarized in 30 minutes<\/p>\n Abstract<\/b>:\u00a0In this welcome seminar I will show a general overview of the research projects I worked on (and I am still working on). In\u00a0the first half, I will focus on my work in the field of Bayesian analysis, specifically on methods for the analysis of high dimensional data\u00a0and Bayesian non-parametrics. In the second half I will focus on more recent work on studying two-way interaction by means of\u00a0biclustering and optimization of research study designs in reliability and agreement studies.<\/p>\n <\/p>\n","protected":false},"excerpt":{"rendered":" Welcome seminar: Alberto Cassese, Giulia Cereda, Cecilia Viscardi The seminar will be held on Friday 2nd December 2022, in Aula 205 (ex 32) (DISIA \u2013 Viale Morgagni 59).\u00a0 ——————– Speaker: …<\/p>\n","protected":false},"author":1,"featured_media":2491,"template":"","meta":{"_monsterinsights_skip_tracking":false,"_monsterinsights_sitenote_active":false,"_monsterinsights_sitenote_note":"","_monsterinsights_sitenote_category":0,"_tribe_events_status":"","_tribe_events_status_reason":"","footnotes":""},"tags":[],"tribe_events_cat":[35],"class_list":["post-4695","tribe_events","type-tribe_events","status-publish","has-post-thumbnail","hentry","tribe_events_cat-seminar","cat_seminar"],"_links":{"self":[{"href":"https:\/\/datascience.unifi.it\/index.php\/wp-json\/wp\/v2\/tribe_events\/4695","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/datascience.unifi.it\/index.php\/wp-json\/wp\/v2\/tribe_events"}],"about":[{"href":"https:\/\/datascience.unifi.it\/index.php\/wp-json\/wp\/v2\/types\/tribe_events"}],"author":[{"embeddable":true,"href":"https:\/\/datascience.unifi.it\/index.php\/wp-json\/wp\/v2\/users\/1"}],"version-history":[{"count":3,"href":"https:\/\/datascience.unifi.it\/index.php\/wp-json\/wp\/v2\/tribe_events\/4695\/revisions"}],"predecessor-version":[{"id":4846,"href":"https:\/\/datascience.unifi.it\/index.php\/wp-json\/wp\/v2\/tribe_events\/4695\/revisions\/4846"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/datascience.unifi.it\/index.php\/wp-json\/wp\/v2\/media\/2491"}],"wp:attachment":[{"href":"https:\/\/datascience.unifi.it\/index.php\/wp-json\/wp\/v2\/media?parent=4695"}],"wp:term":[{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/datascience.unifi.it\/index.php\/wp-json\/wp\/v2\/tags?post=4695"},{"taxonomy":"tribe_events_cat","embeddable":true,"href":"https:\/\/datascience.unifi.it\/index.php\/wp-json\/wp\/v2\/tribe_events_cat?post=4695"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}
\nThe evaluation is based on the calculation of the likelihood ratio, but the likelihood of the data under the competing hypotheses is\u00a0unknown. The “rare type match problem” is the situation in which the matching DNA profile is not in the database of reference, hence\u00a0it is difficult to have an idea of its frequency in the population. In the last years, I have proposed and analyzed different models and\u00a0methods (frequentist, Bayesian, parametric and non-parametric) to evaluate the LR for the rare type match case. They are based on\u00a0quite diverse assumptions and data reduction, and deserve a comparative framework to compare such contributions both theoretically,\u00a0discussing their rationales, and empirically, by assessing their performances through some validation experiments and appropriate\u00a0metrics. This is realized by tailoring to the rare type match problem the \u00a0ECE (Empirical Cross Entropy) plots, a graphical tool based on\u00a0information theory that allows to study the accuracy of each method according to their discrimination power and calibration.<\/p>\n
\nSpeaker: <\/span>CECILIA VISCARDI<\/span><\/p>\n
\nSpeaker: <\/span>ALBERTO CASSESE<\/span><\/p>\n