Well, the argument I often end up making is that you can do a kind of face validation with the fake data. Show it to someone who's used to dealing with that sort of data and if the fake data looks a lot like the data they normally deal with, then maybe more data-taking isn't necessary. If it looks fake to the "expert", then more data-taking is definitely needed.
On 4/19/20 8:29 AM, Marcus Daniels wrote: > I have a hard time with this as a way to extend data. If it is > high-dimensional it will be under-sampled. Seems better to me to measure or > simulate more so that the joint distribution can be realistic. And if you > can do that there is no reason to infer the joint distribution because you > *have* it. > >> On Apr 19, 2020, at 8:18 AM, Frank Wimberly <[email protected]> wrote: >> >> >> Going back and forth: If you infer the causal graph from observational data >> you can use that graph to simulate data with the same joint distribution as >> the original data. -- ☣ uǝlƃ .-. .- -. -.. --- -- -..-. -.. --- - ... -..-. .- -. -.. -..-. -.. .- ... .... . ... FRIAM Applied Complexity Group listserv Zoom Fridays 9:30a-12p Mtn GMT-6 bit.ly/virtualfriam unsubscribe http://redfish.com/mailman/listinfo/friam_redfish.com archives: http://friam.471366.n2.nabble.com/ FRIAM-COMIC http://friam-comic.blogspot.com/
