Timeline for Alternatives to Bayesian statistics when distributions are unknown
Current License: CC BY-SA 4.0
7 events
| when toggle format | what | by | license | comment | |
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| Jun 21, 2021 at 11:28 | vote | accept | stevew | ||
| Jun 21, 2021 at 11:28 | comment | added | stevew |
Thanks everyone for your replies. I've never heard of ABC but from the description it sounded like it's what I'm after. Will read up on this. Thanks!
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| Jun 20, 2021 at 17:46 | comment | added | Accidental Statistician | @Xi'an Yes, thanks, that was poorly worded. There are rare examples where you have the inverse cumulative distribution function for data generation, but the density function is what is required for MCMC. | |
| Jun 20, 2021 at 13:21 | comment | added | Xi'an | @AccidentalStatistician: ABC requires a specific (generative) model as the distribution of the data (or of the summary statistic), hence one "knows" this distribution if not its density function. | |
| Jun 20, 2021 at 9:16 | comment | added | Accidental Statistician | To elaborate, ABC is used when you don't know the distribution of the data, but, given parameter values, you have a way to sample from it. | |
| Jun 20, 2021 at 7:25 | history | edited | Tim | CC BY-SA 4.0 |
added 584 characters in body
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| Jun 20, 2021 at 7:18 | history | answered | Tim | CC BY-SA 4.0 |