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Rerun Latent GP with method="svd" for MvNormals #784

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Rerun Latent GP with method="svd" for MvNormals #784

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267 changes: 120 additions & 147 deletions examples/gaussian_processes/GP-Latent.ipynb
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16 changes: 9 additions & 7 deletions examples/gaussian_processes/GP-Latent.myst.md
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Original file line number Diff line number Diff line change
Expand Up @@ -5,9 +5,9 @@ jupytext:
format_name: myst
format_version: 0.13
kernelspec:
display_name: pymc-examples
display_name: ptdev
language: python
name: pymc-examples
name: python3
myst:
substitutions:
extra_dependencies: jax numpyro
Expand Down Expand Up @@ -119,7 +119,9 @@ cov_func = eta_true**2 * pm.gp.cov.ExpQuad(1, ell_true)
mean_func = pm.gp.mean.Zero()

# The latent function values are one sample from a multivariate normal
f_true = pm.draw(pm.MvNormal.dist(mu=mean_func(X), cov=cov_func(X)), 1, random_seed=rng)
f_true = pm.draw(
pm.MvNormal.dist(mu=mean_func(X), cov=cov_func(X), method="svd"), 1, random_seed=rng
)

# The observed data is the latent function plus a small amount of T distributed noise
# The standard deviation of the noise is `sigma`, and the degrees of freedom is `nu`
Expand Down Expand Up @@ -163,7 +165,7 @@ with pm.Model() as model:
) # add one because student t is undefined for degrees of freedom less than one
y_ = pm.StudentT("y", mu=f, lam=1.0 / sigma, nu=nu, observed=y)

idata = pm.sample(nuts_sampler="numpyro")
idata = pm.sample()
```

```{code-cell} ipython3
Expand Down Expand Up @@ -313,7 +315,7 @@ K = cov_func(x[:, None]).eval()
mean = np.zeros(n)

# sample from the gp prior
f_true = pm.draw(pm.MvNormal.dist(mu=mean, cov=K), 1, random_seed=rng)
f_true = pm.draw(pm.MvNormal.dist(mu=mean, cov=K, method="svd"), 1, random_seed=rng)

# Sample the GP through the likelihood
y = pm.Bernoulli.dist(p=pm.math.invlogit(f_true)).eval()
Expand Down Expand Up @@ -354,7 +356,7 @@ with pm.Model() as model:
p = pm.Deterministic("p", pm.math.invlogit(f))
y_ = pm.Bernoulli("y", p=p, observed=y)

idata = pm.sample(1000, chains=2, cores=2, nuts_sampler="numpyro")
idata = pm.sample(1000, chains=2, cores=2)
```

```{code-cell} ipython3
Expand Down Expand Up @@ -442,7 +444,7 @@ plt.legend(loc=(0.32, 0.65), frameon=True);

```{code-cell} ipython3
%load_ext watermark
%watermark -n -u -v -iv -w -p pytensor,aeppl,xarray
%watermark -n -u -v -iv -w -p pytensor,xarray
```

:::{include} ../page_footer.md
Expand Down