Approximate Bayesian computational methods on-line

[This article was first published on Xi'an's Og » R, and kindly contributed to R-bloggers]. (You can report issue about the content on this page here)
Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.

Fig. 4 – Boxplots of the evolution [against ε] of ABC approximations to the Bayes factor. The representation is made in terms of frequencies of visits to [accepted proposals from] models MA(1) and MA(2) during an ABC simulation when ε corresponds to the 10,1,.1,.01% quantiles on the simulated autocovariance distances. The data is a time series of 50 points simulated from a MA(2) model. The true Bayes factor is then equal to 17.71, corresponding to posterior probabilities of 0.95 and 0.05 for the MA(2) and MA(1) models, resp.

The survey we wrote with Jean-Michel Marin, Pierre Pudlo, and Robin Ryder is now published in [the expensive] Statistics and Computing (on-line). Beside recycling a lot of Og posts on ABC, this paper has the (personal) appeal of giving us the first hint that all was not so rosy in terms of ABC model choice. I wonder whether or not it will be part of the ABC special issue.

Filed under: R, Statistics, University life Tagged: ABC, ARMA models, Bayes factor, Bayesian model choice, Statistics and Computing, sufficiency, summary statistics

To leave a comment for the author, please follow the link and comment on their blog: Xi'an's Og » R. offers daily e-mail updates about R news and tutorials about learning R and many other topics. Click here if you're looking to post or find an R/data-science job.
Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.

Never miss an update!
Subscribe to R-bloggers to receive
e-mails with the latest R posts.
(You will not see this message again.)

Click here to close (This popup will not appear again)