Mathematics Webinar | Bayesian Networks and Their Real-World Applications for Decision Making
Mathematics Webinar | Bayesian Networks and Their Real-World Applications for Decision Making
Part of the MDPI Mathematics Webinars series
Bayesian Networks, Dynamic Bayesian Networks, Decision Sciences, Score Functions, Prior Distribution, Joint Probability
Welcome from the Chair
11th Mathematics Webinar
Bayesian Networks and Their Real-World Applications for Decision Making
Bayesian networks are probabilistic graphical models developed in the late 1980's by Lauritzen and Spiegelhalter (1988) and Pearl (1988), with an easy and detailed introduction by Jensen (2001). They represent a convergence between statistical methodology, data mining, and machine learning. The joint, multidimensional aspect of a BN makes this methodology so attractive for the analysis of complex data. These structures are remarkable for their ability to express a set of complex relationships in a simple manner. Thus, they represent an ideal tool to deal with problems of uncertainty and complexity. A recent overview of their different applications is available in Lauritzen (2003). Due to their interdisciplinary and interconnected characteristics, these tools are applied to several real-world contexts. During this webinar, we will discover some of those applications.
Date: 10 October 2024
Time: 11:00 a.m. CEST | 5:00 a.m. EDT | 5:00 p.m. CST Asia
Webinar ID: 867 1882 3750
Webinar Secretariat: journal.webinar@mdpi.com
Event Chairs
Marta Pittavino is an Assistant Professor in Statistics at the Ca' Foscari University of Venice, Italy. She received her B.Sc. and M.Sc. in Mathematics from the University of Turin in 2009 and 2011, respectively. She received her Ph.D. in Biostatistics from the University of Zurich in 2016. Her research interests include additive Bayesian networks and Bayesian hierarchical models applied to epidemiological studies, the choice of suitable priors, statistical data analysis, regression models, and forecasting methods.
Invited Speakers
Marco Scutari is a Senior Researcher at the Dalle Molle Institute for Artificial Intelligence, one of Switzerland's national research centres. Previously, he held positions in statistics, statistical genetics, and machine learning in Switzerland and the UK. He completed his Ph.D. in Statistics in 2011. His research focuses on the theory of Bayesian networks and their applications to biological and clinical data, as well as statistical computing and software engineering. Dr. Scutari is the main author of the bnlearn package for Bayesian networks, the fairml package for fair machine learning modelling, and the "Bayesian Networks: With Examples in R" and "The Pragmatic Programmer for Machine Learning" books published by CRC Press.
Cinzia Tarantino is a researcher at the University of Geneva. She received her Ph.D. in Information Systems from the University of Geneva in 2024. Her research focuses on a novel concept of risk in complex environments - more specifically, complex risk modeling using probabilistic graphical models such as a Bayesian network.
Webinar Recording
Program
Speaker/Presentation |
Time in CEST |
Time in EDT |
|
Prof. Dr. Marta Pittavino Chair Introduction |
11:00 a.m. - 11:10 a.m. |
5:00 a.m. - 5:10 a.m. |
|
Dr. Marco Scutari Causal Modelling in Space and Time |
11:10 a.m. – 11:40 a.m. |
5:10 a.m. – 5:40 a.m. |
|
Q&A |
11:40 a.m. - 12:00 p.m. |
5:40 a.m. - 6:00 a.m. |
|
Dr. Cinzia Tarantino Data-driven Risk Analysis of Nonlinear Factor Interactions in Road Safety Using Bayesian Networks |
12:00 p.m. - 12:30 p.m. |
6:00 a.m. – 6:30 a.m. |
|
Q&A |
12:30 p.m. – 12:50 p.m. |
6:30 a.m. – 6:50 a.m. |
|
Prof. Dr. Marta Pittavino Concluding Remarks |
12:50 p.m. – 1:00 p.m. |
6:50 a.m. – 7:00 a.m. |
Relevant Special Issue
Guest Editors: Dr. Marta Pittavino
Deadline for manuscript submissions: 1 April 2025
Sponsors and Partners
Organizers
