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Check generative distribution emitting an observed real/int/.. and with no outgoing edges
Doctor the fixed real/int value to be actually modifiable
Then at each iteration:
Compute the likelihood, compute and store importance weight
Keep a copy of observation
Forward sample the observation, record
Roll back to the observation
At the end,
sort the values
normalize the weights
build a alpha% CI for a bunch of alphas
plots these
Can also do for first vs second half of sample to help disentangle model vs inference errors. Also plot the ESS for these GoF estimates to make sure they are reliable. For facetted can be seen as residuals (plot 25, 50, 75)
The text was updated successfully, but these errors were encountered:
At the end,
Can also do for first vs second half of sample to help disentangle model vs inference errors. Also plot the ESS for these GoF estimates to make sure they are reliable. For facetted can be seen as residuals (plot 25, 50, 75)
The text was updated successfully, but these errors were encountered: