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…

webinars

Recent Advancements in Applied Instrumental Variable Methods

2022-02-07

Recent Advancements in Applied Instrumental Variable Methods Instrumental variables (IV) is one of most important and widespread research designs in economics and statistics, as it can identify causal effects in the presence of unobserved confounding. Over the past 30 years the science of IV has…

bayesian-statistics

Measuring dependence in the Wasserstein distance for Bayesian nonparametric models

Marta Catalano, Antonio Lijoi and Igor Prünster / 2022-01-17

Bayesian nonparametric (BNP) models are a prominent tool for performing flexible inference with a natural quantification of uncertainty. Traditionallly, flexible inference within a homogeneous sample is performed with exchangeable models of the type \(X_1,\dots, X_n|\tilde \mu \sim T(\tilde \mu)\),…