September 23, 2024 - 12:30 p.m. Di Fresco Hall, 4th floor 

Speaker: Leonardo Egidi (University of Trieste) 

Title: Prediction, skepticism, and the Bayes Factory All interested parties are invited to attend

Abstract
Nowadays a Bayesian model needs to be reproducible, generative, predictive, robust, computationally scalable, and able to provide sound inferential conclusions. In this wide framework, Bayes factors still represent one of the most well-known and commonly adopted tools to perform model selection and hypothesis testing; however, they are usually criticized due to their intrinsic lack of calibration, and they are rarely used to measure the predictive accuracy arising from competing models. 
We propose two distinct approaches relying on BFs from our most recent research. With regard to prediction, we propose a new algorithmic protocol to transform Bayes factors into measures that evaluate the pure and intrinsic predictive capabilities of models in terms of posterior predictive distributions, by assessing some preliminary theoretical properties and investigating how properly using these tools in clinical trials (joint works with Ioannis Ntzoufras and Roberto Macrì Demartino). 
Then, regarding the analysis of replication studies (Held, 2020), we follow the stream outlined by Pawel and Held (2022) and propose a skeptical mixture prior which represents the prior of an investigator who is unconvinced by the original findings. Its novelty lies in the fact that it incorporates skepticism while controlling for prior-data conflict (Egidi et al., 2021). Consistency properties of the resulting skeptical BF are provided together with a thorough analysis of the main features of our proposal (joint works with Guido Consonni, Roberto Macrì Demartino, Leonhard Held and Samuel Pawel).