Did anybody replicate the ADVI examples in the "Automatic Variational Inference in Stan" paper?

[junpenglao]

Since the theta in Figure 8 is generated from a Gamma distribution, it is already positive. In PyMC3 you just need to specify the right shape, and then tt.sort it along the right axis.

You can also try to use an order transformation (by @aseyboldt), it’s not in our code base yet so if it doesnt work ask @aseyboldt

Mixture models and breaking class symmetry

Ah, I posted old code that used to work in slightly different circumstances. Lesson for next time: Always execute code before posting… This should work (tested on master): class Ordered(pm.distributions.transforms.ElemwiseTransform): name = "ordered" def forward(self, x): out = tt.zeros(x.shape) out = tt.inc_subtensor(out[0], x[0]) out = tt.inc_subtensor(out[1:], tt.log(x[1:] - x[:-1])) return out def forward_val(self, x, point=None): x…