nprofile1qy2hwumn8ghj7un9d3shjtnddaehgu3wwp6kyqpqxn93y37uhtugjz523y402pmuzct9f2dtspf6cz3s44jwx4e4tk9qx7h94j (nprofile…h94j) Interesting, I've never though about assessing the probability that way around before. It would be quite straightforward to do it with a date calibrated in R or Python (like the one in the image). For example, in R:
Ua75650 <- c14::c14_calibrate(1116, 30)
Ua75650[[1]] |>
dplyr::filter(age <= 1060, age > 970) |> # 890–980 AD
dplyr::pull(pdens) |>
sum()
#> [1] 0.8014018
So 80.1%
OxCal is great software, but people do tend to treat it as a black box...
Joe Roe
npub1xu…uhu99
2024-11-20 08:18:45 UTC
in reply to nevent1q…ns6f
Author Public Key
npub1xuqwyzkdnxgwxmdkj88dk6vjs5gjmxuklvhpfsrutpkzv0yq9tjqkuhu99Seen on
wss://relay.ditto.pubPublished at
2024-11-20 08:18:45 UTCEvent JSON
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"content": "nostr:nprofile1qy2hwumn8ghj7un9d3shjtnddaehgu3wwp6kyqpqxn93y37uhtugjz523y402pmuzct9f2dtspf6cz3s44jwx4e4tk9qx7h94j Interesting, I've never though about assessing the probability that way around before. It would be quite straightforward to do it with a date calibrated in R or Python (like the one in the image). For example, in R:\n\nUa75650 \u003c- c14::c14_calibrate(1116, 30)\n\nUa75650[[1]] |\u003e\n dplyr::filter(age \u003c= 1060, age \u003e 970) |\u003e # 890–980 AD\n dplyr::pull(pdens) |\u003e\n sum()\n#\u003e [1] 0.8014018\n\nSo 80.1%\n\nOxCal is great software, but people do tend to treat it as a black box...",
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