Created
December 5, 2022 01:23
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Understanding KL divergence as explained on statistical rethinking from Prof Mcelreath
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| p <- c(0.3, 0.7 ) | |
| N = 1000 | |
| qs <- cbind( seq(0, 1, length.out = N), seq(1, 0, length.out = N) ) | |
| kldivergence <- function(q1, q2) {p <- c(0.3, 0.7 ); sum( p*log(p/c(q1,q2)))} | |
| kldivergence_results <- mapply(kldivergence, qs[,1], qs[,2]) | |
| plot(qs[,1], kldivergence_results, pch=20, col='blue') |
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