I have to verify this, but I think what's causing this is that limits saturate suddenly. The bias against each direction should increase gradually as you approach the limit or something
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Ah I think I have it. Using Matt's algorithm here, you'd get a pointwise entry/exit path counts, and you would toss the coin to actually reflect the number of paths in each direction to generate a uniform sampling of all paths.https://twitter.com/fiddlemath/status/1178035545349345280 …
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Curious if this could just be a different-shaped visualization of a normal distribution, kind of like a bean machine?
https://en.wikipedia.org/wiki/Bean_machine … -
No, this won't be a normal distribution. It's probably 2 crossed poisson distributions, since it's a discrete variable (coin toss)
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What happens when you fix the switching value at 0.5? If you reach the eastern border you then just walk up the boundary.
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Then you generate a strong SE bias because and your sampling has an off-diagonal bias. That was what I did initially. Now I've fixed the diagonal bias, but not the terminal bias.
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