webinars

Nonparametric inference based on statistical depth

2022-10-03

Nonparametric inference based on statistical depth Monday, October 17th, 7:00 PT / 10:00 ET / 16:00 CET The notion of center of an object, be it a set of observations, a physical object or a random variable, is difficult to define. This motivated the development of general ways to measure centrality…

webinars

Recent challenges in model specification testing based on different data structures

2022-09-29

Recent challenges in model specification testing based on different data structures Wednesday, November 9th, 8:00 PT / 11:00 ET / 17:00 CET Model specification testing is one of the essential methodological tasks in statistics. Recently, with the development of different data structures, envisioning…

webinars

Regularization by Noise for Stochastic Differential and Stochastic Partial Differential Equations

2022-06-03

Regularization by Noise for Stochastic Differential and Stochastic Partial Differential Equations The regularizing effects of noisy perturbations of differential equations is a central subject of stochastic analysis. Recent breakthroughs initiated a new wave of interest, particularly concerning…

webinars

Theory and Methods for Inference in Multi-armed Bandit Problems

2022-04-19

Theory and Methods for Inference in Multi-armed Bandit Problems Multi-armed bandit (MAB) algorithms have been argued for decades as useful to conduct adaptively-randomized experiments. By skewing the allocation of the arms towards the more efficient or informative ones, they have the potential to…

webinars

Selection of Priors in Bayesian Structural Equation Modeling

2022-02-14

Selection of Priors in Bayesian Structural Equation Modelling Structural equation modeling (SEM) is an important 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…

webinars

Recent Advances in Approximate Bayesian Inference

2022-02-08

Recent Advances in Approximate Bayesian Inference In approximate Bayesian computation, likelihood function is intractable and needs to be itself estimated using forward simulations of the statistical model (Beaumont et al., 2002; Marin et al., 2012; Sisson et al., 2019; Martin et al., 2020). Recent…