Bayesian restricted likelihood with insufficient statistic [slides]
A great Bayesian Analysis webinar this afternoon with well-balanced presentations by Steve MacEachern and John Lewis, and original discussions by Bertrand Clarke and Fabrizio Rugieri. Which attracted 122 participants. I particularly enjoyed Bertrand’s points that likelihoods were more general than models [made in 6 different wordings!] and that this paper was closer to the M-open perspective. I think I eventually got the reason why the approach could be seen as an ABC with ε=0, since the simulated y’s all get the right statistic, but this presentation does not bring a strong argument in favour of the restricted likelihood approach, when considering the methodological and computational effort. The discussion also made me wonder if tools like VAEs could be used towards approximating the distribution of T(y) conditional on the parameter θ. This is also an opportunity to thank my friend Michele Guindani for his hard work as Editor of Bayesian Analysis and in particular for keeping the discussion tradition thriving!
Related
This entry was posted on February 9, 2022 at 4:00 pm and is filed under Books, pictures, Statistics, University life with tags ABC, Approximate Bayesian computation, approximate inference, Bayesian Analysis, conditional sampling, discussion, information, insufficiency, likelihood function, M-open inference, measure zero set, restricted likelihood, slideshare, Università Ca' Foscari Venezia, variational autoencoders. 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.
This site uses Akismet to reduce spam. Learn how your comment data is processed.
Leave a Reply