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Dependent density notebook update#706

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@fonnesbeckfonnesbeck commentedSep 21, 2024•
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Updated code to v5, sampling to NUTS

📚 Documentation preview 📚:https://pymc-examples--706.org.readthedocs.build/en/706/

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review-notebook-appbot commentedSep 28, 2024•
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jessegrabowski commented on 2024-09-28T05:36:17Z
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Line #9.        beta = pm.Normal("beta", 0.0, 5.0, dims=("one", "K"))

I quite dislike thisone dummy dimension. We could eliminate it and use broadcast multiplication instead of a dot product. The dot seems inappropriate -- the shapes appear to be(n_obs, 1), (1, K),which is just an outer product between two vectors. So make everything 1d and usept.outer.


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review-notebook-appbot commentedSep 28, 2024•
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jessegrabowski commented on 2024-09-28T05:36:18Z
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Line #10.        x = pm.Data("x", std_range)

Add dims?


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review-notebook-appbot commentedSep 28, 2024•
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jessegrabowski commented on 2024-09-28T05:36:19Z
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Remove references to PyMC3 here


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review-notebook-appbot commentedSep 28, 2024•
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jessegrabowski commented on 2024-09-28T05:36:19Z
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Line #4.        mu = pm.Deterministic("mu", gamma + x @ delta)

Same comment as above,delta should havedims=("K",),and writegamma + pt.outer(x, delta)

Also mu is missing dims


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review-notebook-appbot commentedSep 28, 2024•
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jessegrabowski commented on 2024-09-28T05:36:20Z
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Line #4.        obs = pm.NormalMixture("obs", w, mu, tau=tau, observed=y)

dims on obs (and y)


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jessegrabowski commented on 2024-09-28T05:36:21Z
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Looks like every draw diverged. Going to need to tune this to make it work with NUTS.


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Made some comments -- there are a few references to PyMC3 that need to be removed, plus a lot of nitpicks. The biggest problem is in the sampling. Every draw is divergent :( Maybe try nutpie?

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Oof, totally missed the divergences. Odd that the resulting fit was so good.

I tried nutpie and numpyro, but it does not seem to like the stick-breaking piece (even after fixing the outer products). Works okay with Metropolis, so for expediency I'm sticking with this.

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review-notebook-appbot commentedSep 30, 2024•
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jessegrabowski commented on 2024-09-30T14:47:46Z
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typo: September 2024


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Random question: we havepm.StickBreakingWeights, is that something that could be used for this model instead of the hand-rolled function?

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We would have to reframe the problem somewhat, asStickBreakingWeights models them as a function of a concentration parameter, rather than the linear model used here.

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Ok. That doesn't sound worth it? I was just curious.

@zaxtaxzaxtax self-requested a reviewJanuary 19, 2025 18:46
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Aside from a small typo this looks LGTM

$$w_i\ |\ x = v_i\ |\ x \cdot \prod_{j = 1}^{i - 1} (1 - v_j\ |\ x).$$

For the LIDAR data set, we use independent normal priors $\alpha_i \sim N(0, 5^2)$ and $\beta_i \sim N(0, 5^2)$. We now express this this model for the conditional mixture weights using`PyMC3`.
For the LIDAR data set, we use independent normal priors $\alpha_i \sim N(0, 5^2)$ and $\beta_i \sim N(0, 5^2)$. We now express this this model for the conditional mixture weights using`PyMC`.
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Typo.

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@zaxtax Thanks! Looks like it needs to be re-approved after the fix.

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@zaxtaxzaxtaxzaxtax approved these changes

@OriolAbrilOriolAbrilAwaiting requested review from OriolAbril

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