Timeline for ABC, compute Bayes factor from posteriors
Current License: CC BY-SA 4.0
7 events
| when toggle format | what | by | license | comment | |
|---|---|---|---|---|---|
| Jan 24, 2019 at 17:05 | comment | added | Simon C. | Oh right! In that case the prior would be useful, I see. Thanks again. | |
| Jan 24, 2019 at 16:57 | comment | added | Xi'an | My remark is a simple one, namely that, to save computing time, the number of simulations from the most expensive model may be cut down with the ABC Bayes factor still available by the correction. Thanks for your nice comments! | |
| Jan 24, 2019 at 16:54 | comment | added | Simon C. | interesting, why the time taken to simulate should enter into consideration here? What if the real data results from generative process acting at really large time scale? Could it be possible to say that, even if one model take much more time than the other (let say ~ 10 time in my case), they are both equal wrt the original process ( ~ 4 millions times)? (By the way: thank you for your answers, not only this one but all the one you give on Cross Validate they are an amazingly rich source of learning people like my. It's awesome to see that you take time to do so.) | |
| Jan 24, 2019 at 16:42 | vote | accept | Simon C. | ||
| Jan 24, 2019 at 15:48 | comment | added | Xi'an | Yes, keeping the same number of simulations for both models avoids the correction by the prior weights, assuming both models take about the same time to simulate. | |
| Jan 24, 2019 at 14:57 | comment | added | Simon C. | Thanks for your answer. Thus if both models are ran the same number of time this normalizing by the prior's ratio should'nt mater right? And thanks for the paper. I new 1 but not 2, and though my summary statistics raise a full world of problem and question that I may ask when I have more time! | |
| Jan 24, 2019 at 12:55 | history | answered | Xi'an | CC BY-SA 4.0 |