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Unable to replicate "Stochastic Volatility model" in PyMC v5.20#7672

AnsweredbyricardoV94
abdullahau asked this question inQ&A
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I have been trying to runthis notebook with the following packages:

Python implementation: CPythonPython version       : 3.12.8IPython version      : 8.31.0pytensor: 2.26.4aeppl   : not installedxarray  : 2025.1.2numpy     : 1.26.4arviz     : 0.20.0matplotlib: 3.10.0pandas    : 2.2.3pymc      : 5.20.0

Running the functionmodel_to_graphviz, I notice that the model is set-up exactly as intended. However, this part of the code returns extremely different results:

withstochastic_vol_model:idata=pm.sample_prior_predictive(500,random_seed=rng)prior_predictive=az.extract(idata,group="prior_predictive")

output

Note that similar to the notebook, I have set the random seed state asrng = np.random.RandomState(1234)

Additionally, while trying to run this bit of code with the RandomState:

withstochastic_vol_model:idata.extend(pm.sample(random_seed=rng))posterior=az.extract(idata)posterior["exp_volatility"]=np.exp(posterior["volatility"])

I get the error:

TypeError: Cannot create a random Generator from a RandomStream object. Please provide a random seed, BitGenerator or Generator instead.

While setting the random generator asrng = np.random.default_rng(1234), I am unable to produce the same results.

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Yes we should use generator not randomstate.

If I remember correctly, the "results" of that notebook are very dependent on the seed, you can try a different value, but they are not very stable. And if they depend on the seed they are not very meaningful either.

Replies: 1 comment

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Yes we should use generator not randomstate.

If I remember correctly, the "results" of that notebook are very dependent on the seed, you can try a different value, but they are not very stable. And if they depend on the seed they are not very meaningful either.

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Answer selected byabdullahau
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