Updating priors vs using more data give different results

[chartl]

This is almost certainly a problem with applying the “width extension with linear decay” multiple times (i.e., this part:

    # what was never sampled should have a small probability but not 0,
    # so we'll extend the domain and use linear approximation of density on it
    x = np.concatenate([[x[0] - 3 * width], x, [x[-1] + 3 * width]])
    y = np.concatenate([[0], y, [0]])

You can basically see it as the bad left tail of the “updated” distribution. This has been noticed before:

@Edderic

I started running into some issues. With the project that I’m working on, a bunch of the variables are unobserved. Since the from_posterior function adds 3 times the width of the old posterior to the left and the right of the old posterior, the new priors of these unobserved variables become much wider than the original prior. So I’m adding some small constant instead. This prevents the blowup of the prior, while still giving the possibility of exploring values it hasn’t sampled in the past.

context: Can traces be used as priors? - #6 by Edderic