Structural equation modeling (SEM) is a framework within the social sciences that encompasses a wide variety of statistical models. Traditionally, estimation of SEMs has relied on maximum likelihood. Unfortunately, there also exist a variety of situations in which maximum likelihood performs subpar. This led researchers to turn to alternative estimation methods, in particular, Bayesian estimation of SEMs or BSEM. However, it is currently unclear how to specify the prior distribution in order to attain the advantages of Bayesian approaches. Speakers: – Milica Miočević, McGill University, Canada – Sonja D. Winter, University of Missouri, USA – Mauricio Garnier-Villarreal, Vrije Universiteit Amsterdam, The Netherlands Discussant: Sara van Erp, Utrecht University, The Netherlands The webinar is part of YoungStatS project of the Young Statisticians Europe initiative (FENStatS) supported by the Bernoulli Society for Mathematical Statistics and Probability and the Institute of Mathematical Statistics (IMS).