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I'm trying to fit a model with an LKJ prior on the covariance matrix using PyMC v4. I'm running the example notebook, available in the repository here; the only change I've made is to switch from
TypeErrorTraceback (mostrecentcalllast)
/tmp/ipykernel_55195/2070945655.pyin<module>1withpm.Model() asm:
---->2packed_L=pm.LKJCholeskyCov("packed_L", n=2, eta=2.0, sd_dist=pm.Exponential.dist(1.0))
~/projects/pymc3_vs_stan/pymc/pymc/distributions/multivariate.pyinLKJCholeskyCov(name, eta, n, sd_dist, compute_corr, store_in_trace, *args, **kwargs)
1390 """
1391# compute Cholesky decomposition->1392packed_chol=_LKJCholeskyCov(name, eta=eta, n=n, sd_dist=sd_dist)
1393ifnotcompute_corr:
1394returnpacked_chol~/projects/pymc3_vs_stan/pymc/pymc/distributions/distribution.pyin__new__(cls, name, rng, dims, initval, observed, total_size, transform, *args, **kwargs)
253# Create the RV and process dims and observed to determine254# a shape by which the created RV may need to be resized.-->255rv_out, dims, observed, resize_shape=_make_rv_and_resize_shape(
256cls=cls, dims=dims, model=model, observed=observed, args=args, rng=rng, **kwargs257 )
~/projects/pymc3_vs_stan/pymc/pymc/distributions/distribution.pyin_make_rv_and_resize_shape(cls, dims, model, observed, args, **kwargs)
152# Create the RV without dims information, because that's not something tracked at the Aesara level.153# If necessary we'll later replicate to a different size implied by already known dims.-->154rv_out=cls.dist(*args, **kwargs)
155ndim_actual=rv_out.ndim156resize_shape=NoneTypeError: dist() missing1requiredpositionalargument: 'dist_params'
Versions and main components
PyMC/PyMC3 Version: v4.0.0b2
Aesara/Theano Version: 2.3.2
Python Version: 3.8.12
Operating system: Ubuntu
How did you install PyMC/PyMC3: (conda/pip) pip install -e . from the repo
Let me know if you have any questions. Thanks for your help!
Best wishes,
Martin
The text was updated successfully, but these errors were encountered:
Hi all,
Description of your problem
I'm trying to fit a model with an LKJ prior on the covariance matrix using PyMC v4. I'm running the example notebook, available in the repository here; the only change I've made is to switch from
to
All runs fine, until we get to this line:
This gives the following error:
Versions and main components
pip install -e .
from the repoLet me know if you have any questions. Thanks for your help!
Best wishes,
Martin
The text was updated successfully, but these errors were encountered: