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 \(X_1,\dots, X_n|\tilde \mu \sim T(\tilde \mu)\),…

robust-statistics

Universal estimation with Maximum Mean Discrepancy (MMD)

Pierre Alquier / 2022-01-13

This is an updated version of a blog post on RIKEN AIP Approximate Bayesian Inference team webpage: https://team-approx-bayes.github.io/blog/mmd/ […] A very old and yet very exciting problem in statistics is the definition of a universal estimator \(\hat{\theta}\). An estimation procedure…

nonparametric-statistics

Optimal disclosure risk assessment

Federico Camerlenghi, Stefano Favaro, Zacharie Naulet and Francesca Panero / 2021-09-30

Protection against disclosure is a legal and ethical obligation for agencies releasing microdata files for public use. Consider a microdata sample \({X}_n=(X_{1},\ldots,X_{n})\) of size \(n\) from a finite population of size \(\bar{n}=n+\lambda n\), with \(\lambda>0\), such that each sample…

bayesian-statistics

Optional stopping with Bayes factors: possibilities and limitations

Rianne de Heide / 2021-06-10

In recent years, a surprising number of scientific results have failed to hold up to continued scrutiny. Part of this ‘replicability crisis’ may be caused by practices that ignore the assumptions of traditional (frequentist) statistical methods (John, Loewenstein, and Prelec 2012). One of these…

webinars

Developments in Bayesian Nonparametrics (updated with slides)

2021-04-06

The second “One World webinar” organized by YoungStatS will take place on April 21st. The focus of this webinar will be on illustrating modern advances in Bayesian Nonparametrics data analysis, discussing challenging theoretical problems and stimulating case-studies within this active…

generalized-linear-models

A Scalable Empirical Bayes Approach to Variable Selection in Generalized Linear Models

Haim Bar, James Booth and Martin T. Wells / 2021-03-13

In the toolbox of most scientists over the past century, there have been few methods as powerful and as versatile as linear regression. The introduction of the generalized linear model (GLM) framework in the 1970’s extended the inferential and predictive capabilities to binary or count data. While…