Xi'an's Og

Botond Szabó gave a BNP webinar last week on the recent paper he wrote with Bocconni colleagues Francesco Pozza and Daniele Durante, to appear in Series B. Which studies the impact of using skew-symmetric approximations of posterior distributions. Skew-symmetric distributions are easy to simulate, either by accept-reject or by exploiting the cdf x pdf structure and the symmetry in the pdf. The Bernstein-von Mises theorem can be expanded to this case, although I am not certain what this means! The main theoretical result is a gain in the magnitude of the approximation, eg in KL, which I did not expected. With questions about the choice of the cdf (which can be automatised when the original posterior is available or when a closed-form approximation replaces it) and of the symmetry point ξ for complex models (which seems to be the MAP by default.) and of the impact on marginal likelihood approximations (if it makes any sense).

This entry was posted on February 26, 2026 at 12:26 am and is filed under Statistics with tags Bayesian nonparametrics, BNP Section, Milano, Series B, skew-Normal distribution, skew-symmetric distribution, Università Bocconi, webinar. You can follow any responses to this entry through the RSS 2.0 feed. You can leave a response, or trackback from your own site.