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This looks really neat. What do you think of adding this sampler to [pymc3](https://github.com/pymc-devs/pymc3) where it would be directly usable in a probabilistic programming framework?
It's pretty…
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It would be awesome to have pymc3 installed
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It should be fairly easy to apply these samplers to a PyMC3 model. Our samplers are written in pure Python too so the speed should be the same. What do you think?
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Currently, conditioning a PyMC3 model means just removing all entries from the posterior samples where the variables lie outside the specified domain. This approach is problematic, since when dealing …
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Hi!
You did great job! I'm really inspired by this repo. I've discovered ASVGD for myself from recent papers:
http://bayesiandeeplearning.org/papers/BDL_21.pdf
https://arxiv.org/pdf/1611.01722.p…
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Just writing to test the waters of whether you think this would make a good addition to PyMC3: https://github.com/pymc-devs/pymc3. Creating a new sampler is pretty straight-forward and would allow it …
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Really liked your paper. What are you thoughts on including this into PyMC3 where we already have HMC and NUTS (see here for the code: https://github.com/pymc-devs/pymc/blob/master/pymc/step_methods/n…
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instead of sklearn GMM. Use either GMM or, even better, Dirichlet process. Ultimately can combine with binary distribution for the atom identity (instead of trying to fit gaussians to what is a catego…
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PyMC3, python 3.84, win10, Jupyter-lab
code from Chap1 Sec: Introducing our first hammer: PyMC3,
import pymc3 as pm
import theano.tensor as tt
with pm.Model() as model:
alpha = 1.0/count_…