# SVGD convergence

**URL:** <https://discourse.pymc.io/t/svgd-convergence/14560>\
**Category:** General\
**Created:** [June 6, 2024, 5:45pm UTC](https://discourse.pymc.io/t/svgd-convergence/14560 "2024-06-06T17:45:47Z")\
**Posts on this page:** 1\
**Page:** 1

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**Author:** ![Simon](https://yyz2.discourse-cdn.com/flex036/user_avatar/discourse.pymc.io/simon/32/5566_2.png) [@Simon](https://discourse.pymc.io/u/Simon)\
**Post date:** [June 6, 2024, 5:45pm UTC](https://discourse.pymc.io/t/svgd-convergence/14560/1 "2024-06-06T17:45:47Z")

</div>

Hi all. A quick question. I’ trying to sample a fairly complex model with SVGD, but I’ve only found two posts regarding SVGD convergence, and I’m still quite puzzled. My current, silly, approach is to do this:

```auto
svgd = pm.SVGD(model=mod, random_seed=33)

tracker = pm.callbacks.Tracker(mean=svgd.approx.mean.eval, std=svgd.approx.std.eval, hist=svgd.approx.histogram.eval)

approx = svgd.fit(callbacks=[tracker])

fig = plt.figure(figsize=(16, 9))
mu_ax = fig.add_subplot(221)
std_ax = fig.add_subplot(222)
hist_ax = fig.add_subplot(212)
mu_ax.plot(np.array(tracker["mean"]).mean(axis=1))
mu_ax.set_title("Mean track")
std_ax.plot(np.array(tracker["std"]).mean(axis=1))
std_ax.set_title("Std track")
hist_ax.plot(np.array(tracker["hist"]).mean(axis=1).T)
hist_ax.set_title("Negative ELBO track");
plt.savefig("svgd_convergence.png", dpi=300)

```

I just followed the approach in here: [Introduction to Variational Inference with PyMC — PyMC example gallery](https://www.pymc.io/projects/examples/en/latest/variational_inference/variational_api_quickstart.html), which produces the plot below but for ADVI convergence. I guess this may not be the correct approach for SVGD (I only used 30 sample as a example). What would be a good way to check SVGD convergence?

 ![svgd_convergence](https://canada1.discourse-cdn.com/flex036/uploads/pymc3/original/2X/b/b7f7a771e8f3e1385ca53f08858d7f2027d2ab5e.png)

Many thanks in advance.