npub1c9m22hkc5h6zgrwkz48crhcpw6vch2rf6j97746ugl3neys86jeqyz9xjd (npub1c9m…9xjd) Alright, principal components is a volume-preserving rotation into a set of un-correlated Gaussians distributions (in your case). Say they have standard deviation sigma_n (n=1...).
"Entropy" information would be sum_n(-log_2(sigma_n)). This might diverge and need renormalising.
Effective degrees of freedom should be 2^entropy. That's
product_n(1/sigma_n).
Stephen Brooks 🦆
npub1uz…pux8m
2024-10-20 01:13:55 UTC
in reply to nevent1q…9jer
Author Public Key
npub1uza23672am24xv9g0fqxstrg6y7r4y28wk6pvpc7mhh9na6tjckq6pux8mPublished at
2024-10-20 01:13:55 UTCEvent JSON
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"content": "nostr:npub1c9m22hkc5h6zgrwkz48crhcpw6vch2rf6j97746ugl3neys86jeqyz9xjd Alright, principal components is a volume-preserving rotation into a set of un-correlated Gaussians distributions (in your case). Say they have standard deviation sigma_n (n=1...).\n\n\"Entropy\" information would be sum_n(-log_2(sigma_n)). This might diverge and need renormalising.\n\nEffective degrees of freedom should be 2^entropy. That's \nproduct_n(1/sigma_n).",
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